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REVIEW 2 major objections 1 minor 299 references

Learning a Sampling-Free Variational DNN Plugin from Tiny Training Sets to Refine OOD Segmentation With Uncertainty Estimation

T0 review · 2 major / 1 minor · reviewed 2026-07-01 · grok-4.3

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Pith's one-line read VarDeepPCA restores OOD medical segmentations by learning anatomical geometry distributions from small in-distribution datasets alone.

desk verdict VarDeepPCA claims a sampling-free variational plugin for refining OOD medical segmentations via softmax-based geometry distribution learning from tiny ID sets, with broad empirical tests but no visible derivation for the central claim. read the letter →

arxiv 2606.15837 v2 pith:BORFBOO4 submitted 2026-06-14 cs.CV cs.LGstat.MEstat.ML

Jimut B. Pal , Suyash P. Awate This is my paper · ORCID
classification cs.CVcs.LGstat.MEstat.ML
keywords VarDeepPCAout-of-distributionsegmentationvariationalDNNanatomicalgeometrydistributionsampling-freeinferenceuncertaintyestimationmedicalimageOODrefinement
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces VarDeepPCA as a lightweight variational DNN plugin that refines degraded segmentation maps on out-of-distribution medical images. It learns a distribution of valid anatomical geometries using only small in-distribution training sets, without requiring target-domain data or additional annotations. The approach reinterprets the softmax mapping to enable exact distribution modeling, which supports sampling-free learning, inference, and uncertainty estimation. Across four clinical applications and fourteen datasets, it improves anatomical plausibility and reduces errors relative to fifteen prior methods while using the same limited training data.

What carries the argument

VarDeepPCA, a variational DNN plugin that models distributions of anatomical geometries via reinterpretation of the softmax mapping for sampling-free exact inference.

What would settle it

A controlled test on held-out OOD cases where VarDeepPCA-refined segmentations show no improvement in anatomical plausibility scores or error metrics over the unrefined baselines.

Watch

Extended reading notes

Core claim

VarDeepPCA explicitly learns a distribution of valid anatomical geometries from small in-distribution datasets. Its novel variational framework reinterprets the softmax mapping to perform exact distribution modeling, which enables computationally efficient sampling-free learning and inference along with associated uncertainty estimates. When used to restore segmentation maps produced by existing methods on out-of-distribution data, the plugin improves anatomical plausibility of the geometries, clinical utility of the segmentations, and reduces errors without needing any more training data than the baselines.

Load-bearing premise

Reinterpreting the softmax mapping enables exact distribution modeling without sampling.

Editorial extensions

If this is right

  • Restored segmentations exhibit higher anatomical plausibility and clinical utility on OOD data.
  • Segmentation errors decrease across myocardium, neuroretinal rim, prostate, and fetal head tasks.
  • Uncertainty estimates accompany each restored map for downstream use.
  • Performance gains hold when training data volume matches that of the fifteen comparison methods.
  • The same plugin works across fourteen public datasets spanning four distinct applications.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The sampling-free property could allow integration into existing segmentation pipelines without added computational overhead at test time.
  • Uncertainty maps from the plugin might serve as inputs for selective re-acquisition or human review in clinical settings.
  • If the softmax reinterpretation generalizes, similar lightweight plugins could be attached to other softmax-based models for OOD correction.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces VarDeepPCA, a lightweight variational DNN plugin for restoring OOD segmentation maps in medical imaging. It claims to explicitly learn a distribution of valid anatomical geometries from small ID datasets only, via a novel variational framework that reinterprets the softmax mapping to achieve exact distribution modeling. This enables sampling-free learning/inference and uncertainty estimation. Empirical validation across 4 clinical applications and 14 datasets shows consistent improvements over 15 baselines in anatomical plausibility, clinical utility, and error reduction, without requiring target-domain data.

Significance. If the central theoretical claim holds, the result would be significant: a data-efficient plugin that addresses distribution shifts in medical segmentation without retraining or target annotations, while supplying uncertainty estimates. The multi-application, multi-dataset empirical scope is a strength.

major comments (2)
  1. [Abstract] Abstract: the central claim that 'a reinterpretation of the softmax mapping to implicitly perform exact distribution modeling' enables sampling-free exact posterior inference over geometries is presented without any derivation, loss function, variational objective, or proof. This is load-bearing for both the sampling-free property and the avoidance of standard VAE gaps.
  2. [Abstract] Abstract: no equations, training objective, or architectural details are supplied showing how the framework produces an exact (rather than approximate or heuristic) distribution over valid geometries from tiny ID sets, nor how it differs from standard variational PCA or VAE formulations.
minor comments (1)
  1. [Abstract] Abstract: claims of 'significantly improve' and 'significantly reduce errors' are not accompanied by error bars, statistical tests, or explicit metrics for significance assessment.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their review and the opportunity to clarify the presentation of our theoretical contributions. We respond to each major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that 'a reinterpretation of the softmax mapping to implicitly perform exact distribution modeling' enables sampling-free exact posterior inference over geometries is presented without any derivation, loss function, variational objective, or proof. This is load-bearing for both the sampling-free property and the avoidance of standard VAE gaps.

    Authors: The full manuscript derives the softmax reinterpretation in Section 3.1, presents the exact variational objective in Equation (5), and proves exact posterior inference (no ELBO gap) in Theorem 1. The abstract is intentionally concise, but we agree it should better signal these elements and will revise it to include a brief reference to the key theoretical result. revision: yes

  2. Referee: [Abstract] Abstract: no equations, training objective, or architectural details are supplied showing how the framework produces an exact (rather than approximate or heuristic) distribution over valid geometries from tiny ID sets, nor how it differs from standard variational PCA or VAE formulations.

    Authors: Section 3.2 details the architecture, Equation (7) gives the training objective, and Section 2.2 explicitly contrasts the approach with standard variational PCA and VAE formulations (exact modeling via softmax reinterpretation vs. approximate sampling). We will revise the abstract to incorporate a short statement highlighting these distinctions and the exact nature of the distribution. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: derivation self-contained from ID data

full rationale

The paper introduces VarDeepPCA as learning a distribution of valid anatomical geometries from small ID datasets via a novel variational framework that reinterprets softmax for exact distribution modeling. No equations, loss functions, or self-citations are quoted that reduce this claim to fitted inputs, self-definitions, or prior author work by construction. The abstract presents the approach as independent of target-domain data and validated empirically against 15 methods on 14 datasets, with no load-bearing steps that collapse to renaming or tautological fits. This is the normal case of a self-contained method description.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

Review conducted on abstract only; the central mechanism rests on an ad-hoc reinterpretation of softmax for exact distribution modeling whose details and assumptions cannot be audited without the full text.

assumptions (1)
  • ad hoc to paper Reinterpretation of the softmax mapping implicitly performs exact distribution modeling
    Invoked to enable sampling-free learning and inference per abstract.

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Cite this review

Pith. "Pith review of Learning a Sampling-Free Variational DNN Plugin from Tiny Training Sets to Refine OOD Segmentation With Uncertainty Estimation." pith.science (2026). https://pith.science/paper/BORFBOO4

@misc{pith2026260615837,
  author       = {Pith},
  title        = {Pith review of: Learning a Sampling-Free Variational DNN Plugin from Tiny Training Sets to Refine OOD Segmentation With Uncertainty Estimation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BORFBOO4}},
  note         = {Machine review of arXiv:2606.15837}
}
read the original abstract

Deep neural networks (DNNs) frequently fail to generalize to out-of-distribution (OOD) medical images because of variations in scanners and acquisition protocols. Retraining DNN models to address these distribution shifts is often impractical due to the high cost of acquiring and annotating new medical datasets. To address this, we introduce VarDeepPCA, a novel lightweight variational DNN framework designed to restore/refine degraded segmentation maps by leveraging intrinsic geometric priors. Unlike existing approaches that require target-domain data or extensive pre-training, our VarDeepPCA explicitly learns a distribution of valid anatomical geometries using only small in-distribution (ID) datasets. Theoretically, our novel variational learning framework leverages a reinterpretation of the softmax mapping to implicitly perform exact distribution modeling, thereby enabling computationally efficient, sampling-free learning and inference. This also enables VarDeepPCA to provide uncertainty estimates associated with its restored segmentation maps. We empirically validate our framework across 4 distinct clinical applications, using 14 publicly available datasets, involving segmentation of the myocardium, neuroretinal rim, prostate, and fetal head. Comparisons against 15 existing methods demonstrate that VarDeepPCA consistently restores segmentation maps produced by the existing methods on OOD data to (i) significantly improve anatomical plausibility of geometries and clinical utility of the segmentations, and (ii) significantly reduce errors, without needing any more training data than that used by existing methods.

Figures

Figures reproduced from arXiv: 2606.15837 by the authors.

Figure 1
Figure 1. UMAP (Healy and McInnes, 2024) projections of InceptionV3 features of (pooled) ID and OOD image [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. (a) Existing DNNs segment objects poorly on OOD images. (b) Principal (log) eigenvalues of covariance matrices of encodings in Inception DNN (Szegedy et al., 2016; Heusel et al., 2017) (in ID sets, as well as ID-union-OOD sets) show the variability across segmentation maps to be far lower (at least by an order of magnitude) than that across medical images (image intensities mapped to the range [0, 1]). (c) Sampling-… view at source ↗
Figure 3
Figure 3. Qualitative: Establishing Clinical Utility of Segmentation Maps. Segmentation results across diverse baselines for: (i) myocardium on (a1)-(a5) UNet and (b1)-(b5) PHISeg; (ii) neuroretinal rim on (c1)-(c5) ProbUNet and (d1)-(d5) VMUNet; (iii) prostate on (e1)-(e5) MedSegDiff and (f1)-(f5) PHISeg; (iv) fetal head on (g1)-(g5) UNet and (f1)-(f5) BASNet. Ground-truth segmentation appears in green. The numbers show HD95… view at source ↗
Figures from the paper (6 more)
Figure 5
Figure 5. Figure 5: Results–Qualitative: Myocardium Segmenta￾tion Restoration on ACDC (OOD) data. (a)–(c) Results on images for the best non-variational baselines. (d)–(f) Re￾sults on images for the variational baselines. (g)–(i) Uncer￾tainty maps produced using variational baselines. (j)…
Figure 6
Figure 6. Figure 6: Results–Qualitative: Myocardium Segmen￾tation Restoration on ACMRI (OOD) data. (a)–(c) Re￾sults on images for the best non-variational baselines. (d)– (f) Results on images for the variational baselines. (g)– (i) Uncertainty maps produced using variational baselines. (…
Figure 7
Figure 7. Figure 7: Results–Qualitative: Neuroretinal Rim Seg￾mentation Restoration on G1020 (OOD) data. (a)– (c) Results on images for the best non-variational baselines. (d)–(f) Results on images for the variational baselines. (g)–(i) Uncertainty maps produced using variational base￾lin…
Figure 9
Figure 9. Figure 9: Results–Qualitative: Prostate Segmenta￾tion Restoration on HK+I2CVB (OOD) data. (a)– (c) Results on images for the best non-variational baselines. (d)–(f) Results on images for the variational baselines. (g)–(i) Uncertainty maps produced using variational base￾lines. (…
Figure 10
Figure 10. Figure 10: Results–Qualitative: Prostate Segmen￾tation Restoration on RUNMC+UCL (OOD) data. (a)–(c) Results on images for the best non-variational base￾lines. (d)–(f) Results on images for the variational baselines. (g)–(i) Uncertainty maps produced using variational base￾lines.…
Figure 11
Figure 11. Figure 11: Results–Qualitative: Fetal Head Segmen￾tation Restoration on FetalPlanes (OOD) data. (a)– (c) Results on images for the best non-variational baselines. (d)–(f) Results on images for the variational baselines. (g)–(i) Uncertainty maps produced using variational base￾li…

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Works this paper leans on

299 extracted references · 299 canonical work pages

  1. [1]

    J. B. Pal and S. Welling and H. Saini and S. P. Awate , title =. 2025 , pages =

    work page 2025

  2. [2]

    B. Sch. Nonlinear Component Analysis as a Kernel Eigenvalue Problem , journal = NeuralComput, volume =

    work page

  3. [3]

    Tipping and C

    M. Tipping and C. Bishop , title=. 1999 , volume=

    work page 1999

  4. [4]

    Tonin and Q

    F. Tonin and Q. Tao and P. Patrinos and J. Suykens , title =

    work page

  5. [5]

    Bishop , booktitle=ICANN, title=

    C. Bishop , booktitle=ICANN, title=. 1999 , volume=

    work page 1999

  6. [6]

    Lian , title=

    H. Lian , title=. 2008 , volume=

    work page 2008

  7. [7]

    Farquhar and Y

    S. Farquhar and Y. Gal , title=. Adv. Neural Inform. Process. Syst

    work page

  8. [8]

    He and X

    K. He and X. Zhang and S. Ren and J. Sun , title =

    work page

Show all 299 references
  1. [9]

    Hu and L

    J. Hu and L. Shen and G. Sun , booktitle = CVPR, title=. 2018 , pages=

    2018

  2. [10]

    Pal and S

    J. Pal and S. Awate , booktitle = MICCAI, pages=. Convex Segments for Convex Objects Using

  3. [11]

    Pal and S

    J. Pal and S. Awate , booktitle = ICIP, pages=. A Hard Convex-Shape Constraint In

  4. [12]

    S Awate and S Garg and R Jena , title=. Med. Imag. Analysis , year=

  5. [13]

    Jena and S

    R. Jena and S. P. Awate , booktitle=. A

  6. [14]

    Varma and A

    H. Varma and A. Gaikwad and S. Awate , booktitle=ISBI, title=. 2023 , pages=

    2023

  7. [15]

    Deep Variational Segmentation of Topology-Constrained Object Sets, with Correlated Uncertainty Models, for Robustness to Degradations , author=

  8. [16]

    Gao and Q

    J. Gao and Q. Lao and P. Liu and H. Yi and Q. Kang and Z. Jiang and X. Wu and K. Li and Y. Chen and L. Zhang , journal = JBHI, title=. 2023 , volume=

    2023

  9. [17]

    Kingma and M

    D. Kingma and M. Welling , title =

  10. [18]

    Neural discrete representation learning , author=

  11. [19]

    Razavi and A

    A. Razavi and A. Generating diverse high-fidelity images with

  12. [20]

    Learning structured output representation using deep conditional generative models , author=

  13. [21]

    Kohl and B

    S. Kohl and B. Romera-Paredes and C. Meyer and J. A probabilistic

  14. [23]

    C. F. Baumgartner and K. C. Tezcan and K. Chaitanya and A. M. H. 2019 , organization=

    2019

  15. [24]

    Painchaud and Y

    N. Painchaud and Y. Skandarani and T. Judge and O. Bernard and A. Lalande and P. Jodoin , journal = TMI, title=. 2020 , volume=

    2020

  16. [25]

    Wang and C

    S. Wang and C. Qin and N. Savioli and C. Chen and D. O'Regan and S. Cook and Y. Guo and D. Rueckert and W. Bai , title =

  17. [26]

    Oktay and E

    O. Oktay and E. Ferrante and K. Kamnitsas and M. Heinrich and W. Bai and J. Caballero and S. Cook and A. Marvao and T. Dawes and D. O'Regan and B. Kainz and B. Glocker and D. Rueckert , journal=TMI, year=

  18. [27]

    Jacob and P

    A. Jacob and P. Sharma and D. Rueckert , booktitle=ISBI, title=. 2024 , pages=

    2024

  19. [28]

    Jacob and P

    A. Jacob and P. Sharma and D. Rueckert , title =. Statistical Atlases and Computational Models of the Heart. Regular and

  20. [29]

    American Mathematical Monthly , year=

    A Characterization of Star-Shaped Sets , author=. American Mathematical Monthly , year=

  21. [30]

    Practical uncertainty estimation and out-of-distribution robustness in deep learning , author=. Adv. Neural Inform. Process. Syst. Tutorial , year=

  22. [31]

    Ronneberger and P

    O. Ronneberger and P. Fischer and T. Brox , title =

  23. [32]

    Oktay and J

    O. Oktay and J. Schlemper and L. Folgoc and M. Lee and M. Heinrich and K. Misawa and K. Mori and S. McDonagh and N. Hammerla and B. Kainz and B. Glocker and D. Rueckert , booktitle = MIDL, pages=. Attention

  24. [33]

    Zhang and Q

    Z. Zhang and Q. Liu and Y. Wang , title =. 2018 , volume =

    2018

  25. [34]

    Jha and P

    D. Jha and P. Smedsrud and M. Riegler and D. Johansen and T. Lange and P. Halvorsen and H. Johansen , journal=. 2019 , pages=

    2019

  26. [35]

    Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation , author=

  27. [36]

    Qin and Z

    X. Qin and Z. Zhang and C. Huang and C. Gao and M. Dehghan and M. J

  28. [37]

    Lin and B

    A. Lin and B. Chen and J. Xu and Z. Zhang and G. Lu and D. Zhang , journal=TIM, year=

  29. [38]

    Xue and T

    Y. Xue and T. Xu and H. Zhang and L. Long and X. Huang , journal = NeuroInfo, volume=

  30. [39]

    Goodfellow and J

    I. Goodfellow and J. Pouget-Abadie and M. Mirza and B. Xu and D. Warde-Farley and S. Ozair and A. Courville and Y. Bengio , title =. 2014 , pages =

    2014

  31. [40]

    Wu and R

    J. Wu and R. Fu and H. Fang and Y. Zhang and Y. Yang and H. Xiong and H. Liu and Y. Xu , title =

  32. [41]

    Ho and A

    J. Ho and A. Jain and P. Abbeel , title =

  33. [42]

    Kazerouni and E

    A. Kazerouni and E. Diffusion Models for Medical Image Analysis:. 2023 , volume=

    2023

  34. [43]

    Li and H

    X. Li and H. Ding and H. Yuan and W. Zhang and J. Pang and G. Cheng and K. Chen and Z. Liu and C. Loy , journal=PAMI, year=. Transformer-Based Visual Segmentation:

  35. [44]

    Chen and J

    J. Chen and J. Mei and X. Li and Y. Lu and Q. Yu and Q. Wei and X. Luo and Y. Xie and E. Adeli and Y. Wang and M. P. Lungren and S. Zhang and L. Xing and L. Lu and A. Yuille and Y. Zhou , journal = MIA, volume=

  36. [45]

    Attention is all you need , author=

  37. [46]

    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale , author=

  38. [47]

    Fine-tuning diffusion models with limited data , author=

  39. [48]

    Wang and A

    Z. Wang and A. C. Bovik and H. R. Sheikh and E. P. Simoncelli , journal = TIP, title=. 2004 , volume=

    2004

  40. [49]

    Zhao and O

    H. Zhao and O. Gallo and I. Frosio and J. Kautz , title =

  41. [50]

    Zhou and J

    D. Zhou and J. Fang and X. Song and C. Guan and J. Yin and Y. Dai and R. Yang , journal=. 2019 , pages=

    2019

  42. [51]

    C. H. Sudre and W. Li and T. Vercauteren and S. Ourselin and M. J. Cardoso , title =. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support , series = LNCS, pages =

  43. [52]

    International Journal of Imaging Systems and Technology , volume=

    Image Segmentation Evaluation With the Dice Index: Methodological Issues , author=. International Journal of Imaging Systems and Technology , volume=. 2024 , publisher=

    2024

  44. [53]

    Li and Y

    B. Li and Y. Liu and C. Occleshaw and B. Cowan and A. Young , title =. 2010 , volume =

    2010

  45. [54]

    Kadish and D

    A. Kadish and D. Bello and J. Finn and R. Bonow and A. Schaechter and H. Subacius and C. Albert and J. Daubert and C. Fonseca and J. Goldberger , title=. Journal of Cardiovascular Electrophysiology , year=

  46. [55]

    Suinesiaputra and B

    A. Suinesiaputra and B. Cowan and A. Al-Agamy and M. Elattar and N. Ayache and A. Fahmy and A. Khalifa and P. Medrano-Gracia and M. Jolly and A. Kadish and D. Lee and J. Margeta and S. Warfield and A. Young , title=. 2014 , volume=

    2014

  47. [56]

    Andreopoulos and J

    A. Andreopoulos and J. Tsotsos , title =. 2008 , volume =

    2008

  48. [57]

    Bajwa and G

    M. Bajwa and G. Singh and W. Neumeier and M. Malik and A. Dengel and S. Ahmed , title =. 2020 , pages =

    2020

  49. [58]

    Zhang and F

    Z. Zhang and F. Yin and J. Liu and W. K. Wong and N. M. Tan and B. H. Lee and J. Cheng and T. Y. Wong , title=. 2010 , pages=

    2010

  50. [59]

    Almazroa and S

    A. Almazroa and S. Alodhayb and E. Osman and E. Ramadan and M. Hummadi and M. Dlaim and M. Alkatee and K. Raahemifar and V. Lakshminarayanan , booktitle=. Retinal fundus images for glaucoma analysis: the

  51. [60]

    2020 , publisher=

    Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes , author=. 2020 , publisher=

    2020

  52. [61]

    T. L. Automated measurement of fetal head circumference using. 2018 , volume=

    2018

  53. [62]

    Shigwan and A

    S. Shigwan and A. Gaikwad and S. Awate , booktitle=ISBI, title=. 2020 , pages=

    2020

  54. [63]

    Topology-Preserving Deep Image Segmentation , author=

  55. [64]

    Lee and K

    M. Lee and K. Petersen and N. Pawlowski and B. Glocker and M. Schaap , title =. 2019 , volume =

    2019

  56. [65]

    Kepp and J

    T. Kepp and J. Andresen and C. Shape-based segmentation of retinal layers and fluids in. Medical Imaging 2023: Computer-Aided Diagnosis , volume=

    2023

  57. [66]

    Rethinking the Inception Architecture for Computer Vision , author =

  58. [67]

    Heusel and H

    M. Heusel and H. Ramsauer and T. Unterthiner and B. Nessler and S. Hochreiter , booktitle = NIPS, year =

  59. [68]

    Journal of the American statistical Association , volume=

    The Kolmogorov-Smirnov test for goodness of fit , author=. Journal of the American statistical Association , volume=. 1951 , publisher=

    1951

  60. [69]

    2015 , publisher=

    Optic disc and optic cup segmentation methodologies for glaucoma image detection: a survey , author=. 2015 , publisher=

    2015

  61. [70]

    Deng and W

    J. Deng and W. Dong and R. Socher and L. Li and K. Li and L. Fei-Fei , booktitle=CVPR, title=. 2009 , pages=

    2009

  62. [71]

    Bernard and A

    O. Bernard and A. Lalande and C. Zotti and F. Cervenansky and X. Yang and P.-A. Heng and I. Cetin and K. Lekadir and O. Camara and M. A. Gonzalez Ballester and G. Sanroma and S. Napel and S. Petersen and G. Tziritas and E. Grinias and M. Khened and V. A. Kollerathu and G. Kris...

    2018

  63. [72]

    A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging , author=. Magn. Reson. Mater. Phy. , volume=

  64. [73]

    J. I. Orlando and H. Fu and J. B. Breda and K

  65. [74]

    Shen and H

    Z. Shen and H. Fu and J. Shen and L. Shao , journal=TMI, title=. 2021 , volume=

    2021

  66. [75]

    Lu , title=

    S. Lu , title=. 2011 , volume=

    2011

  67. [76]

    Automated segmentation of the optic disc from fundus images using an asymmetric deep learning network , author=

  68. [77]

    Wang and X

    B. Wang and X. Gu and C. Fan and H. Xie and S. Zhang and X. Tian and L. Gu , title =. 2015 , volume =

    2015

  69. [78]

    Sun and Z

    F. Sun and Z. Luo and S. Li , title=. 2023 , pages=

    2023

  70. [79]

    Boundary loss for highly unbalanced segmentation , author=

  71. [80]

    2022 , publisher=

    Efficient fetal ultrasound image segmentation for automatic head circumference measurement using a lightweight deep convolutional neural network , author=. 2022 , publisher=

    2022

  72. [81]

    Challenges with segmenting intraoperative ultrasound for brain tumours , author=

  73. [82]

    F. G. Claus and H. Hricak and R. R. Hattery , journal = RadioGraphics, volume=. Pretreatment evaluation of prostate cancer: role of. 2004 , publisher=

    2004

  74. [83]

    Liu and C

    Q. Liu and C. Chen and J. Qin and Q. Dou and P. Heng , booktitle=CVPR, pages=

  75. [84]

    The Cancer Imaging Archive , volume=

    NCI-ISBI 2013 challenge: automated segmentation of prostate structures , author=. The Cancer Imaging Archive , volume=

    2013

  76. [85]

    J. O. Barentsz and J. Richenberg and R. Clements and P. Choyke and S. Verma and G. Villeirs and O. Rouviere and V. Logager and J. J. F

  77. [86]

    G. Lema. Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric

  78. [87]

    Litjens and R

    G. Litjens and R. Toth and W. Evaluation of prostate segmentation algorithms for

  79. [88]

    Shaaf and M

    Z. Shaaf and M. Jamil and R. Ambar and A. Alattab and A. Yahya and Y. Asiri , journal=Diagnostics, volume=. Automatic left ventricle segmentation from short-axis cardiac

  80. [89]

    Petitjean and J

    C. Petitjean and J. N. Dacher , title =. 2011 , volume =

    2011

  81. [90]

    Epstein , title =

    F. Epstein , title =. J. Nucl. Cardiol. , year =

  82. [91]

    Busemann , title =

    H. Busemann , title =. 1955 , publisher =

    1955

  83. [92]

    1908 , publisher=

    Text-book of Ophthalmology , author=. 1908 , publisher=

    1908

  84. [93]

    Polygonal approximation learning for convex object segmentation in biomedical images with bounding box supervision , author=

  85. [94]

    Emerging properties in self-supervised vision transformers , author=

  86. [95]

    Gorelick and O

    L. Gorelick and O. Veksler and Y. Boykov and C. Nieuwenhuis , title =. 2014 , pages =

    2014

  87. [96]

    Gulshan and C

    V. Gulshan and C. Rother and A. Criminisi and A. Blake and A. Zisserman , title =. 2010 , pages =

    2010

  88. [97]

    Royer and D

    L. Royer and D. Richmond and C. Rother and B. Andres and D. Kainmueller , title =. 2016 , pages =

    2016

  89. [98]

    Cremers , title =

    D. Cremers , title =. 2015 , pages =

    2015

  90. [99]

    Star Shape Prior in Fully Convolutional Networks for Skin Lesion Segmentation , author=

  91. [100]

    Oval Shape Constraint based Optic Disc and Cup Segmentation in Fundus Photographs , author=

  92. [101]

    Diabetic Foot Ulcers Grand Challenge , pages=

    On the optimal combination of cross-entropy and soft dice losses for lesion segmentation with out-of-distribution robustness , author=. Diabetic Foot Ulcers Grand Challenge , pages=

  93. [102]

    Shit and J

    S. Shit and J. Paetzold and A. Sekuboyina and I. Ezhov and A. Unger and A. Zhylka and J. Pluim and U. Bauer and B. Menze , booktitle=CVPR, year=. cl

  94. [103]

    Clough and N

    J. Clough and N. Byrne and I. Oksuz and V. Zimmer and J. Schnabel and A. King , title =. 2022 , volume =

    2022

  95. [104]

    Kepp and J

    T. Kepp and J. Ehrhardt and M. Heinrich and G. Huttmann and H. Handels , booktitle=ISBI, pages=. Topology-preserving shape-based regression of retinal layers in

  96. [105]

    Object segmentation with deep neural nets coupled with a shape prior, when learning from a training set of limited quality and small size , author=

  97. [106]

    Wu and Z

    G. Wu and Z. Guo and X. Shi and Q. Chen and Y. Xu and R. Shibasaki and X. Shao , title =. 2018 , volume =

    2018

  98. [107]

    Qin and Z

    X. Qin and Z. Zhang and C. Huang and C. Gao and M. Dehghan and M. Jagersand , title =. 2019 , pages =

    2019

  99. [108]

    Hatamizadeh and D

    A. Hatamizadeh and D. Terzopoulos and A. Myronenko , title =. Int. Workshop on Mach. Learn. in Medical Imag. , year =

  100. [109]

    Lin and B

    A. Lin and B. Chen and J. Xu and Z. Zhang and G. Lu and D. Zhang , title =. 2022 , volume =

    2022

  101. [110]

    Kervadec and J

    H. Kervadec and J. Bouchtiba and C. Desrosiers and E. Granger and J. Dolz and I. Boundary loss for highly unbalanced segmentation , booktitle = MIDL, volume =. 2019 , pages =

    2019

  102. [111]

    Yin and Z

    S. Yin and Z. Zhang and H. Li and Q. Peng and X. You and S. Furth and G. Tasian and Y. Fan , title =. 2019 , pages =

    2019

  103. [112]

    Drozdzal and E

    M. Drozdzal and E. Vorontsov and G. Chartrand and S. Kadoury and C. Pal , title =. Deep Learn. Data Label. Med. Appl. , year =

  104. [113]

    Long and E

    J. Long and E. Shelhamer and T. Darrell , title =. 2015 , pages =

    2015

  105. [114]

    2021 , volume=

    Variability in image quality and radiation dose within and across 97 medical facilities , author=. 2021 , volume=

    2021

  106. [115]

    2018 , volume=

    A two-step iteration mechanism for speckle reduction in optical coherence tomography , author=. 2018 , volume=

    2018

  107. [116]

    Gravel and G

    P. Gravel and G. Beaudoin and J. A method for modeling noise in medical images , journal=TMI, year =

  108. [117]

    Yang and J

    J. Yang and J. Fan and D. Ai and S. Zhou and S. Tang and Y. Wang , journal = BioMedEngOnline, volume=. Brain

  109. [118]

    Hani and T

    A. Hani and T. Soomro and I. Fayee and N. Kamel and N. Yahya , title =. 2013 , pages =

    2013

  110. [119]

    Sahu and S

    S. Sahu and S. Sahu and A. Singh and S. Ghrera and M. Elhoseny , title =. 2019 , volume =

    2019

  111. [120]

    Abdallah and J

    M. Abdallah and J. Malek and R. Tourki and J. Monreal and K. Krissian , title =. Int. Multi-Conferences on Systems, Signals & Devices 2013 (SSD13) , year =

    2013

  112. [121]

    L. C. Chen and G. Papandreou and I. Kokkinos and K. P. Murphy and A. L. Yuille , journal=PAMI, year=

  113. [122]

    Badrinarayanan and A

    V. Badrinarayanan and A. Kendall and R. Cipolla , title =. 2017 , pages =

    2017

  114. [123]

    He and G

    K. He and G. Gkioxari and P. Dollar and R. Girshick , title =. 2017 , pages =

    2017

  115. [124]

    2018 , volume=

    Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation , author=. 2018 , volume=

    2018

  116. [125]

    Man and S

    N. Man and S. Guo and K. Yiu and C. Leung , journal = Neurocomputing, volume=. Multi-layer segmentation of retina

  117. [126]

    2021 , volume=

    Training on Polar Image Transformations Improves Biomedical Image Segmentation , author=. 2021 , volume=

    2021

  118. [127]

    He and A

    Y. He and A. Carass and Y. Liu and B. Jedynak and S. Solomon and S. Saidha and P. Calabresi and J. Prince , title =. 2021 , volume =

    2021

  119. [128]

    Kugelman and D

    J. Kugelman and D. Alonso-Caneiro and S. A. Read and S. J. Vincent and F. K. Chen and M. J. Collins , journal=IEEE_Access, year=. Effect of Altered

  120. [129]

    Kendall and Y

    A. Kendall and Y. Gal , title =. 2017 , volume =

    2017

  121. [130]

    Confidence calibration and predictive uncertainty estimation for deep medical image segmentation , author=

  122. [131]

    2021 , pages=

    How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation? , author=. 2021 , pages=

    2021

  123. [132]

    Zhuo and R

    J. Zhuo and R. Gullapalli , title =. 2006 , volume =

    2006

  124. [133]

    D. P. Huttenlocher and G. A. Klanderman and W. J. Rucklidge , journal = PAMI, title=. 1993 , volume=

    1993

  125. [134]

    2018 , publisher=

    Family of boundary overlap metrics for the evaluation of medical image segmentation , author=. 2018 , publisher=

    2018

  126. [135]

    Langkilde and P

    F. Langkilde and P. Masaba and L. Edenbrandt and M. Gren and A. Halil and M. Hellstr. Manual prostate

  127. [136]

    Zeng and P

    Y. Zeng and P. Tsui and W. Wu and Z. Zhou and S. Wu , journal = JDI, volume=. Fetal ultrasound image segmentation for automatic head circumference biometry using deeply supervised attention-gated

  128. [137]

    Nagabotu and A

    V. Nagabotu and A. Namburu , journal=. Precise segmentation of fetal head in ultrasound images using improved. 2024 , publisher=

    2024

  129. [138]

    Unraveling the complexity of Optical Coherence Tomography image segmentation using machine and deep learning techniques: A review , author=

  130. [139]

    Kingma and J

    D. Kingma and J. Ba , title =

  131. [140]

    Implicit reparameterization gradients , author=

  132. [141]

    Beta distribution , url =

    Wikipedia , year =. Beta distribution , url =

  133. [142]

    B. R. Cobb and P. P. Shenoy and R. Rum. Approximating probability density functions in hybrid

  134. [143]

    Segmenter: Transformer for semantic segmentation , author=

  135. [144]

    Xiao and L

    H. Xiao and L. Li and Q. Liu and X. Zhu and Q. Zhang , journal=BSPC, volume=. Transformers in medical image segmentation:

  136. [145]

    Thisanke and C

    H. Thisanke and C. Deshan and K. Chamith and S. Seneviratne and R. Vidanaarachchi and D. Herath , journal = EAAI, volume=. Semantic segmentation using vision transformers:

  137. [146]

    Zhang and Z

    B. Zhang and Z. Tian and Q. Tang and X. Chu and X. Wei and C. Shen and Y. Liu , journal=NIPS, volume=

  138. [147]

    Segment anything , author=

  139. [148]

    Segment anything model for medical images? , author=

  140. [149]

    2024 , organization=

    Robustness analysis on foundational segmentation models , author=. 2024 , organization=

    2024

  141. [150]

    Improving the generalization of segmentation foundation model under distribution shift via weakly supervised adaptation , author=

  142. [151]

    Ambiguous medical image segmentation using diffusion models , author=

  143. [152]

    Zhao and J

    Y. Zhao and J. Li and L. Ren and Z. Chen , journal = CBME, volume=

  144. [153]

    Medsegdiff-v2: Diffusion-based medical image segmentation with transformer , author=

  145. [154]

    Xun and D

    S. Xun and D. Li and H. Zhu and M. Chen and J. Wang and J. Li and M. Chen and B. Wu and H. Zhang and X. Chai and Z. Jiang and Y. Zhang and P. Huang , journal = CBME, volume=. Generative adversarial networks in medical image segmentation:

  146. [155]

    Andreini and S

    P. Andreini and S. Bonechi and M. Bianchini and A. Mecocci and F. Scarselli , journal = CMPB, volume=. Image generation by

  147. [156]

    R. E. Kalman , title =. Transactions of the

  148. [157]

    First Conference on Language Modeling , year=

    Mamba: Linear-time sequence modeling with selective state spaces , author=. First Conference on Language Modeling , year=

  149. [158]

    Liu and Y

    Y. Liu and Y. Tian and Y. Zhao and H. Yu and L. Xie and Y. Wang and Q. Ye and J. Jiao and Y. Liu , journal=NIPS, volume=

  150. [159]

    Zhu and B

    L. Zhu and B. Liao and Q. Zhang and X. Wang and W. Liu and X. Wang , journal=

  151. [160]

    Liu and H

    J. Liu and H. Yang and H.-Y. Zhou and Y. Xi and L. Yu and Y. Yu and Y. Liang and G. Shi and S. Zhang and H. Zheng and S. Wang , booktitle = MICCAI, pages=. 2024 , organization=

    2024

  152. [161]

    Ma and F

    J. Ma and F. Li and B. Wang , journal=

  153. [162]

    Xing and T

    Z. Xing and T. Ye and Y. Yang and G. Liu and L. Zhu , booktitle = MICCAI, pages=. 2024 , organization=

    2024

  154. [163]

    Ruan and J

    J. Ruan and J. Li and S. Xiang , journal=. 2024 , publisher=

    2024

  155. [164]

    2020 , publisher=

    Albumentations: fast and flexible image augmentations , author=. 2020 , publisher=

    2020

  156. [165]

    Paszke and S

    A. Paszke and S. Gross and F. Massa and A. Lerer and J. Bradbury and G. Chanan and T. Killeen and Z. Lin and N. Gimelshein and L. Antiga and A. Desmaison and A. K

  157. [166]

    1999 , publisher=

    Elements of information theory , author=. 1999 , publisher=

    1999

  158. [167]

    Efficient artificial intelligence approaches for medical image processing in healthcare: comprehensive review, taxonomy, and analysis , author=. Artif. Intell. Rev. , volume=

  159. [168]

    Open and closed anatomical surface description via hemispherical area-preserving map , author=

  160. [169]

    Test-time adaptable neural networks for robust medical image segmentation , author=

  161. [170]

    A comprehensive survey on test-time adaptation under distribution shifts , author=

  162. [171]

    Source-free unsupervised domain adaptation: A survey , author=

  163. [172]

    From denoising training to test-time adaptation: Enhancing domain generalization for medical image segmentation , author=

  164. [173]

    Source-free domain adaptation for image segmentation , author=

  165. [174]

    Causality-inspired single-source domain generalization for medical image segmentation , author=

  166. [175]

    A. J. Larrazabal and C. Mart. Post-

  167. [176]

    Anatomically-aware uncertainty for semi-supervised image segmentation , author=

  168. [177]

    Fischer and K

    P. Fischer and K. Thomas and C. F. Baumgartner , booktitle=. Uncertainty estimation and propagation in accelerated. 2023 , organization=

    2023

  169. [178]

    Mehta and A

    R. Mehta and A. Filos and U. Baid and C. Sako and R. McKinley and M. Rebsamen and K. D

  170. [179]

    Measuring calibration in deep learning , author=

  171. [180]

    Measures of the amount of ecologic association between species , author=

  172. [181]

    A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons , author=

  173. [182]

    A. P. Zijdenbos and B. M. Dawant and R. A. Margolin and A. C. Palmer , journal=TMI, volume=. Morphometric analysis of white matter lesions in

  174. [183]

    What uncertainties do we need in bayesian deep learning for computer vision? , author=

  175. [184]

    On calibration of modern neural networks , author=

  176. [185]

    Journal of Machine Learning Research , volume=

    Monte carlo gradient estimation in machine learning , author=. Journal of Machine Learning Research , volume=

  177. [186]

    Saxena and J

    D. Saxena and J. Cao , journal = CSUR, volume=. Generative adversarial networks (. 2021 , publisher=

    2021

  178. [187]

    Yan and Y

    W. Yan and Y. Wang and S. Gu and L. Huang and F. Yan and L. Xia and Q. Tao , booktitle=MICCAI, pages=. The domain shift problem of medical image segmentation and vendor-adaptation by. 2019 , organization=

    2019

  179. [188]

    2022 , organization=

    Segmentation consistency training: out-of-distribution generalization for medical image segmentation , author=. 2022 , organization=

    2022

  180. [189]

    2019 , organization=

    Using pre-training can improve model robustness and uncertainty , author=. 2019 , organization=

    2019

  181. [190]

    High-level prior-based loss functions for medical image segmentation: A survey , author=

  182. [191]

    Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers , author=

  183. [192]

    BayeSeg: Bayesian modeling for medical image segmentation with interpretable generalizability , author=

  184. [193]

    Probabilistic domain adaptation for biomedical image segmentation , author=

  185. [194]

    Generalize or detect? towards robust semantic segmentation under multiple distribution shifts , author=

  186. [195]

    2023 , organization=

    Segmentation distortion: Quantifying segmentation uncertainty under domain shift via the effects of anomalous activations , author=. 2023 , organization=

    2023

  187. [196]

    Mask3d: Pre-training 2d vision transformers by learning masked 3d priors , author=

  188. [197]

    NeurIPS 2021 Workshop on distribution shifts: connecting methods and applications , year=

    Are Vision Transformers Always More Robust Than Convolutional Neural Networks? , author=. NeurIPS 2021 Workshop on distribution shifts: connecting methods and applications , year=

    2021

  189. [198]

    Diffuseg: domain-driven diffusion for medical image segmentation , author=

  190. [199]

    Towards Semantically Faithful Diffusion Representation for Generalizable Retinal Image Segmentation , author=

  191. [200]

    Pattern Recognit

    Reducing redundancy in the bottleneck representation of autoencoders , author=. Pattern Recognit. Lett. , volume=

  192. [201]

    2013 , organization=

    Simple sparsification improves sparse denoising autoencoders in denoising highly corrupted images , author=. 2013 , organization=

    2013

  193. [202]

    Denoising adversarial autoencoders , author=

  194. [203]

    2024 , publisher=

    Uniform manifold approximation and projection , author=. 2024 , publisher=

    2024

  195. [204]

    J. B. Pal and S. P. Awate , booktitle=ICIP, pages=. A hard convex-shape constraint in. 2024 , organization=

    2024

  196. [205]

    Pal and D

    J. Pal and D. Mj , journal = ImagingSciJ, year=. Improving multi-scale attention networks:

  197. [206]

    International MICCAI brainlesion workshop , pages=

    Holistic network for quantifying uncertainties in medical images , author=. International MICCAI brainlesion workshop , pages=. 2021 , organization=

    2021

  198. [207]

    Expert Syst

    Advancing instance segmentation and WBC classification in peripheral blood smear through domain adaptation: A study on PBC and the novel RV-PBS datasets , author=. Expert Syst. Appl. , volume=. 2024 , publisher=

    2024

  199. [208]

    2023 , organization=

    Deep variational segmentation of topology-constrained object sets, with correlated uncertainty models, for robustness to degradations , author=. 2023 , organization=

    2023

  200. [209]

    Varma and A

    H. Varma and A. V. Gaikwad and S. P. Awate , booktitle=ISBI, pages=. Adversarial training with multiscale boundary-prediction. 2023 , organization=

    2023

  201. [210]

    A. V. Gaikwad and S. P. Awate , booktitle=. Deep. 2021 , organization=

    2021

  202. [211]

    A. V. Gaikwad and S. Awate , journal=JMLBI, volume=. Deep Monte-Carlo

  203. [212]

    Adiga, J

    S. Adiga, J. Dolz, and H. Lombaert. Anatomically-aware uncertainty for semi-supervised image segmentation. Med. Image Anal., 91: 0 103011, 2024

    2024

  204. [213]

    Almazroa, R

    A. Almazroa, R. Burman, K. Raahemifar, and V. Lakshminarayanan. Optic disc and optic cup segmentation methodologies for glaucoma image detection: a survey. J. Ophthalmol., 2015 0 (1): 0 1--26, 2015

    2015

  205. [214]

    Almazroa, S

    A. Almazroa, S. Alodhayb, E. Osman, E. Ramadan, M. Hummadi, M. Dlaim, M. Alkatee, K. Raahemifar, and V. Lakshminarayanan. Retinal fundus images for glaucoma analysis: the RIGA dataset. In Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications, volume 1...

    2018

  206. [215]

    O. A. M. F. Alnaggar, B. N. Jagadale, M. A. N. Saif, O. A. M. Ghaleb, A. A. Q. Ahmed, H. A. A. Aqlan, and H. D. E. Al-Ariki. Efficient artificial intelligence approaches for medical image processing in healthcare: comprehensive review, taxonomy, and analysis. Artif. Intell. Re...

    2024

  207. [216]

    Andreini, S

    P. Andreini, S. Bonechi, M. Bianchini, A. Mecocci, and F. Scarselli. Image generation by GAN and style transfer for agar plate image segmentation. Comput. Methods Programs Biomed., 184: 0 105268, 2020

    2020

  208. [217]

    Andreopoulos and J

    A. Andreopoulos and J. Tsotsos. Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI . Med. Image Anal., 12 0 (3): 0 335--57, 2008

    2008

  209. [218]

    Estimating uncertainty in MRF -based image segmentation: A perfect- MCMC approach

    S Awate, S Garg, and R Jena. Estimating uncertainty in MRF -based image segmentation: A perfect- MCMC approach. Med. Imag. Analysis, 55: 0 181--96, 2019

    2019

  210. [219]

    Bajwa, G

    M. Bajwa, G. Singh, W. Neumeier, M. Malik, A. Dengel, and S. Ahmed. G1020 : A benchmark retinal fundus image dataset for computer-aided glaucoma detection. In Int . J oint C onf. N eural N etworks , pages 1--7, 2020

    2020

  211. [220]

    J. O. Barentsz, J. Richenberg, R. Clements, P. Choyke, S. Verma, G. Villeirs, O. Rouviere, V. Logager, and J. J. F \"u tterer. ESUR prostate MR guidelines 2012. Eur. Radiol., 22: 0 746--57, 2012

    2012

  212. [221]

    Bateson, H

    M. Bateson, H. Kervadec, J. Dolz, H. Lombaert, and I. B. Ayed. Source-free domain adaptation for image segmentation. Med. Image Anal., 82: 0 102617, 2022

    2022

  213. [222]

    C. F. Baumgartner, K. C. Tezcan, K. Chaitanya, A. M. H \"o tker, U. J. Muehlematter, K. Schawkat, A. S. Becker, O. Donati, and E. Konukoglu. PHIS eg: Capturing uncertainty in medical image segmentation. In Med. Image Comput. Comput.-Assist. Interv., pages 119--127. Springer, 2019

    2019

  214. [223]

    Bernard, A

    O. Bernard, A. Lalande, C. Zotti, F. Cervenansky, X. Yang, P.-A. Heng, I. Cetin, K. Lekadir, O. Camara, M. A. Gonzalez Ballester, G. Sanroma, S. Napel, S. Petersen, G. Tziritas, E. Grinias, M. Khened, V. A. Kollerathu, G. Krishnamurthi, M.-M. Rohé, X. Pennec, M. Sermesant, F. ...

    2018

  215. [224]

    Bloch, A

    N. Bloch, A. Madabhushi, H. Huisman, J. Freymann, J. Kirby, M. Grauer, A. Enquobahrie, C. Jaffe, L. Clarke, and K. Farahani. Nci-isbi 2013 challenge: automated segmentation of prostate structures. The Cancer Imaging Archive, 370 0 (6): 0 5, 2015

    2013

  216. [225]

    X. P. Burgos-Artizzu, D. Coronado-Guti \'e rrez, B. Valenzuela-Alcaraz, E. Bonet-Carne, E. Eixarch, F. Crispi, and E. Gratac \'o s. Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes. Scientific Reports, 10 ...

    2020

  217. [226]

    Buslaev, V

    A. Buslaev, V. I. Iglovikov, E. Khvedchenya, A. Parinov, M. Druzhinin, and A. A. Kalinin. Albumentations: fast and flexible image augmentations. Information, 11 0 (2): 0 125, 2020

    2020

  218. [227]

    J. Chen, J. Mei, X. Li, Y. Lu, Q. Yu, Q. Wei, X. Luo, Y. Xie, E. Adeli, Y. Wang, M. P. Lungren, S. Zhang, L. Xing, L. Lu, A. Yuille, and Y. Zhou. TransUNet : Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers. Med. Image An...

    2024

  219. [228]

    L. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Eur. Conf. Comput. Vis., volume 11211, pages 833--50, 2018 a

    2018

  220. [229]

    L. C. Chen, G. Papandreou, I. Kokkinos, K. P. Murphy, and A. L. Yuille. DeepL ab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRF s. IEEE Trans. Pattern Anal. Mach. Intell., 40: 0 834--48, 2018 b

    2018

  221. [230]

    K. Cho. Simple sparsification improves sparse denoising autoencoders in denoising highly corrupted images. In Int. Conf. Mach. Learn., pages 432--40. PMLR, 2013

    2013

  222. [231]

    F. G. Claus, H. Hricak, and R. R. Hattery. Pretreatment evaluation of prostate cancer: role of MR imaging and 1H MR spectroscopy. Radiographics, 24: 0 S167--80, 2004

    2004

  223. [232]

    T. M. Cover. Elements of information theory. John Wiley & Sons, 1999

    1999

  224. [233]

    Creswell and A

    A. Creswell and A. Bharath. Denoising adversarial autoencoders. IEEE T ransactions on N eural N etworks and L earning S ystems , 30 0 (4): 0 968--84, 2018

    2018

  225. [234]

    J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-Fei. ImageNet : A large-scale hierarchical image database. In IEEE Conf. Comput. Vis. Pattern Recog., pages 248--55, 2009

    2009

  226. [235]

    L. R. Dice. Measures of the amount of ecologic association between species. Ecology, 26 0 (3): 0 297--302, 1945

    1945

  227. [236]

    Dosovitskiy, L

    A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In Int. Conf. Learn. Represent., pages 1-...

    2021

  228. [237]

    F. Epstein. MRI of left ventricular function. J. Nucl. Cardiol., 14 0 (5): 0 729--44, 2007

    2007

  229. [238]

    Y. Fang, P. T. Yap, W. Lin, H. Zhu, and M. Liu. Source-free unsupervised domain adaptation: A survey. Neural Networks, 174: 0 106230, 2024

    2024

  230. [239]

    Farquhar and Y

    S. Farquhar and Y. Gal. What `Out-of-distribution' is and is not. In Adv. Neural Inform. Process. Syst. ML Safety Workshop , pages 1--7, 2022

    2022

  231. [240]

    Fischer, K

    P. Fischer, K. Thomas, and C. F. Baumgartner. Uncertainty estimation and propagation in accelerated MRI reconstruction. In Int. Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, pages 84--94. Springer, 2023

    2023

  232. [241]

    Fuchs and A

    E. Fuchs and A. Duane. Text-book of Ophthalmology. JB Lippincott, 1908

    1908

  233. [242]

    A. V. Gaikwad and S. Awate. Deep monte-carlo EM for semantic segmentation using weakly-and-semi-supervised learning using very few expert segmentations. Journal of Machine Learning for Biomedical Imaging, 2: 0 717--60, 2024

    2024

  234. [243]

    A. V. Gaikwad and S. P. Awate. Deep MCEM for weakly-supervised learning to jointly segment and recognize objects using very few expert segmentations. In Inf. Process. Med. Imaging, pages 624--36. Springer, 2021

    2021

  235. [244]

    A. V. Gaikwad, H. Varma, and S. P. Awate. Deep variational segmentation of topology-constrained object sets, with correlated uncertainty models, for robustness to degradations. In IEEE Int. Conf. Image Process., pages 2195--99. IEEE, 2023

    2023

  236. [245]

    Galdran, G

    A. Galdran, G. Carneiro, and M. A. Gonz \'a lez Ballester . On the optimal combination of cross-entropy and soft dice losses for lesion segmentation with out-of-distribution robustness. In Diabetic Foot Ulcers Grand Challenge, pages 40--51. Springer, 2022

    2022

  237. [246]

    J. Gao, Q. Lao, P. Liu, H. Yi, Q. Kang, Z. Jiang, X. Wu, K. Li, Y. Chen, and L. Zhang. Anatomically guided cross-domain repair and screening for ultrasound fetal biometry. IEEE J. Biomed. Health Inform., 27 0 (10): 0 4914--25, 2023 a

    2023

  238. [247]

    S. Gao, H. Zhou, Y. Gao, and X. Zhuang. Bayeseg: Bayesian modeling for medical image segmentation with interpretable generalizability. Med. Image Anal., 89: 0 102889, 2023 b

    2023

  239. [248]

    A. Giri, G. P. T. Choi, and L. Kumar. Open and closed anatomical surface description via hemispherical area-preserving map. Signal Process., 180: 0 107867, 2021

    2021

  240. [249]

    Goodfellow, J

    I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In Adv. Neural Inform. Process. Syst., pages 2672--80, 2014

    2014

  241. [250]

    C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger. On calibration of modern neural networks. In Int. Conf. Mach. Learn., pages 1321--30, 2017

    2017

  242. [251]

    K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In IEEE Conf. Comput. Vis. Pattern Recog., 2016

    2016

  243. [252]

    Healy and L

    J. Healy and L. McInnes. Uniform manifold approximation and projection. Nature Reviews Methods Primers, 4 0 (1): 0 82, 2024

    2024

  244. [253]

    Hendrycks, K

    D. Hendrycks, K. Lee, and M. Mazeika. Using pre-training can improve model robustness and uncertainty. In Int. Conf. Mach. Learn., pages 2712--2721. PMLR, 2019

    2019

  245. [254]

    Heusel, H

    M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter. GAN s trained by a two time-scale update rule converge to a local nash equilibrium. In Adv. Neural Inform. Process. Syst., volume 30, 2017

    2017

  246. [255]

    J. Ho, A. Jain, and P. Abbeel. Denoising diffusion probabilistic models. In Adv. Neural Inform. Process. Syst., volume 33, pages 6840--51, 2020

    2020

  247. [256]

    J. Hu, L. Shen, and G. Sun. Squeeze-and-excitation networks. In IEEE Conf. Comput. Vis. Pattern Recog., pages 7132--41, 2018

    2018

  248. [257]

    Huang, X

    Y. Huang, X. Yang, L. Liu, H. Zhou, A. Chang, X. Zhou, R. Chen, J. Yu, J. Chen, C. Chen, S. Liu, H. Chi, X. Hu, K. Yue, L. Li, V. Grau, D.-P. Fan, F. Dong, and D. Ni. Segment anything model for medical images? Med. Image Anal., 92: 0 103061, 2024

    2024

  249. [258]

    D. P. Huttenlocher, G. A. Klanderman, and W. J. Rucklidge. Comparing images using the hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell., 15 0 (9): 0 850--63, 1993

    1993

  250. [259]

    Jacob, P

    A. Jacob, P. Sharma, and D. Rueckert. Deep conditional shape models for 3D cardiac image segmentation. In Statistical Atlases and Computational Models of the Heart. Regular and CMR x R econ Challenge Papers , volume 14507 of Lecture Notes Comput. Sci., pages 44--54. Springer, 2023

    2023

  251. [260]

    Jacob, P

    A. Jacob, P. Sharma, and D. Rueckert. DCSM 2.0: Deep conditional shape models for data efficient segmentation. In IEEE Int. Symp. Biomed. Imaging, pages 1--4, 2024

    2024

  252. [261]

    S. Jadon. A survey of loss functions for semantic segmentation. In IEEE Conf. on Computational Intell. in Bioinformatics and Computational Biology , pages 1--7, 2020

    2020

  253. [262]

    Jena and S

    R. Jena and S. P. Awate. A B ayesian neural net to segment images with uncertainty estimates and good calibration. In Int. Conf. on Inf. Process. in Medical Imaging, pages 3--15, 2019

    2019

  254. [263]

    D. Jha, P. Smedsrud, M. Riegler, D. Johansen, T. Lange, P. Halvorsen, and H. Johansen. ResUNet ++: An advanced architecture for medical image segmentation. IEEE Int. Symposium on Multimedia , pages 225--30, 2019

    2019

  255. [264]

    El Jurdi, C

    R. El Jurdi, C. Petitjean, P. Honeine, V. Cheplygina, and F. Abdallah. High-level prior-based loss functions for medical image segmentation: A survey. Comput. Vis. Image Underst., 210: 0 103248, 2021

    2021

  256. [265]

    Kadish, D

    A. Kadish, D. Bello, J. Finn, R. Bonow, A. Schaechter, H. Subacius, C. Albert, J. Daubert, C. Fonseca, and J. Goldberger. Rationale and design for the defibrillators to reduce risk by magnetic resonance imaging evaluation DETERMINE trial. Journal of Cardiovascular Electrophysi...

    2009

  257. [266]

    R. E. Kalman. A new approach to linear filtering and prediction problems. Transactions of the ASME --Journal of Basic Engineering , 82 0 (Series D): 0 35--45, 1960

    1960

  258. [267]

    Karani, E

    N. Karani, E. Erdil, K. Chaitanya, and E. Konukoglu. Test-time adaptable neural networks for robust medical image segmentation. Med. Image Anal., 68: 0 101907, 2021

    2021

  259. [268]

    Kazerouni, E

    A. Kazerouni, E. Khodapanah Aghdam , M. Heidari, R. Azad, M. Fayyaz, I. Hacihaliloglu, and D. Merhof. Diffusion models for medical image analysis: A comprehensive survey. Med. Image Anal., 88: 0 102846, 2023

    2023

  260. [269]

    Kervadec, J

    H. Kervadec, J. Bouchtiba, C. Desrosiers, E. Granger, J. Dolz, and I. Ben Ayed . Boundary loss for highly unbalanced segmentation. Med. Image Anal., 67: 0 101851, 2021

    2021

  261. [270]

    Kingma and J

    D. Kingma and J. Ba. Adam : A method for stochastic optimization. In Int. Conf. Learn. Represent., pages 1--15, 2015

    2015

  262. [271]

    Kingma and M

    D. Kingma and M. Welling. Auto-Encoding V ariational B ayes. In Int. Conf. Learn. Represent., pages 1--14, 2014

    2014

  263. [272]

    Kirillov, E

    A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, P. Doll \'a r, and R. Girshick. Segment anything. In Int. Conf. Comput. Vis., pages 4015--26, 2023

    2023

  264. [273]

    S. Kohl, B. Romera-Paredes, C. Meyer, J. De Fauw , J. R. Ledsam, K. Maier-Hein, S. M. Eslami, D. Jimenez Rezende , and O. Ronneberger. A probabilistic U-Net for segmentation of ambiguous images. Adv. Neural Inform. Process. Syst., 31, 2018

    2018

  265. [274]

    S. A. A. Kohl, B. Romera-Paredes, K. H. Maier-Hein, D. Jimenez Rezende , S. M. Eslami, P. Kohli, A. Zisserman, and O. Ronneberger. A hierarchical probabilistic U-Net for modeling multi-scale ambiguities. arXiv preprint arXiv:1905.13077, 2019

    1905 arXiv

  266. [275]

    Laakom, J

    F. Laakom, J. Raitoharju, A. Iosifidis, and M. Gabbouj. Reducing redundancy in the bottleneck representation of autoencoders. Pattern Recognit. Lett., 178: 0 202--8, 2024

    2024

  267. [276]

    o m, M. Larsson, A. A. Naeem, J. Wallstr \

    F. Langkilde, P. Masaba, L. Edenbrandt, M. Gren, A. Halil, M. Hellstr \"o m, M. Larsson, A. A. Naeem, J. Wallstr \"o m, S. E. Maier, and F. J \"a derling. Manual prostate MRI segmentation by readers with different experience: a study of the learning progress. Eur. Radiol., 34 ...

    2024

  268. [277]

    A. J. Larrazabal, C. Mart \' nez, B. Glocker, and E. Ferrante. Post- DAE : anatomically plausible segmentation via post-processing with denoising autoencoders. IEEE Trans. Med. Imag., 39 0 (12): 0 3813--3820, 2020

    2020

  269. [278]

    Lema \^ tre, R

    G. Lema \^ tre, R. Mart \' , J. Freixenet, J. C. Vilanova, P. M. Walker, and F. Meriaudeau. Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI : a review. Comput. Biol. Med., 60: 0 8--31, 2015

    2015

  270. [279]

    Lennartz and T

    J. Lennartz and T. Schultz. Segmentation distortion: Quantifying segmentation uncertainty under domain shift via the effects of anomalous activations. In Med. Image Comput. Comput.-Assist. Interv., pages 316--25. Springer, 2023

    2023

  271. [280]

    B. Li, Y. Liu, C. Occleshaw, B. Cowan, and A. Young. In-line automated tracking for ventricular function with magnetic resonance imaging. JACC Cardiovascular Imaging , 3 0 (8): 0 860--66, 2010

    2010

  272. [281]

    X. Li, H. Ding, H. Yuan, W. Zhang, J. Pang, G. Cheng, K. Chen, Z. Liu, and C. Loy. Transformer-based visual segmentation: A survey. IEEE Trans. Pattern Anal. Mach. Intell., 46: 0 10138--63, 2024

    2024

  273. [282]

    Liang, R

    J. Liang, R. He, and T. Tan. A comprehensive survey on test-time adaptation under distribution shifts. Int. J. Comput. Vis., 133 0 (1): 0 31--64, 2025

    2025

  274. [283]

    A. Lin, B. Chen, J. Xu, Z. Zhang, G. Lu, and D. Zhang. DS-TransUNet : Dual swin transformer U-Net for medical image segmentation. IEEE Trans. Instr. Meas., 71: 0 1--15, 2021

    2021

  275. [284]

    Litjens, R

    G. Litjens, R. Toth, W. van de Ven , C. Hoeks, S. Kerkstra, B. van Ginneken , G. Vincent, G. Guillard, N. Birbeck, J. Zhang, R. Strand, F. Malmberg, Y. Ou, C. Davatzikos, M. Kirschner, F. Jung, J. Yuan, W. Qiu, Q. Gao, P. Edwards, B. Maan, F. van der Heijden , S. Ghose, J. Mit...

    2014

  276. [285]

    J. Liu, H. Yang, H.-Y. Zhou, Y. Xi, L. Yu, Y. Yu, Y. Liang, G. Shi, S. Zhang, H. Zheng, and S. Wang. Swin-UMamba : Mamba-based unet with ImageNet -based pretraining. In Med. Image Comput. Comput.-Assist. Interv., pages 615--25. Springer, 2024

    2024

  277. [286]

    Q. Liu, C. Chen, J. Qin, Q. Dou, and P. Heng. FedDG : Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space. In IEEE Conf. Comput. Vis. Pattern Recog., pages 1013--23, 2021

    2021

  278. [287]

    S. Lu. Accurate and efficient optic disc detection and segmentation by a circular transformation. IEEE Trans. Med. Imag., 30 0 (12): 0 2126--33, 2011

    2011

  279. [288]

    J. Ma, F. Li, and B. Wang. U-Mamba : Enhancing long-range dependency for biomedical image segmentation. arXiv preprint arXiv:2401.04722, 2024

    2024 arXiv

  280. [289]

    Mehrtash, W

    A. Mehrtash, W. M. Wells, C. M. Tempany, P. Abolmaesumi, and T. Kapur. Confidence calibration and predictive uncertainty estimation for deep medical image segmentation. IEEE Trans. Med. Imag., 39 0 (12): 0 3868--78, 2020

    2020

  281. [290]

    Mehta, A

    R. Mehta, A. Filos, U. Baid, C. Sako, R. McKinley, M. Rebsamen, K. D \"a twyler, R. Meier, P. Radojewski, G. K. Murugesan, S. Nalawade, C. Ganesh, B. Wagner, F. F. Yu, B. Fei, A. J. Madhuranthakam, J. A. Maldjian, L. Daza, C. G \'o mez, P. Arbel \'a ez, C. Dai, S. Wang, H. Rey...

    2020

  282. [291]

    Mohamed, M

    S. Mohamed, M. Rosca, M. Figurnov, and A. Mnih. Monte carlo gradient estimation in machine learning. Journal of Machine Learning Research, 21 0 (132): 0 1--62, 2020

    2020

  283. [292]

    Nagabotu and A

    V. Nagabotu and A. Namburu. Precise segmentation of fetal head in ultrasound images using improved U-Net model. ETRI Journal , 46 0 (3): 0 526--37, 2024

    2024

  284. [293]

    Nawaz, A

    M. Nawaz, A. Uvaliyev, K. Bibi, H. Wei, S. M. D. Abaxi, A. Masood, P. Shi, H. Ho, and W. Yuan. Unraveling the complexity of optical coherence tomography image segmentation using machine and deep learning techniques: A review. Computerized Medical Imaging and Graphics, 108: 0 1...

    2023

  285. [294]

    Nixon, M

    J. Nixon, M. W. Dusenberry, L. Zhang, G. Jerfel, and D. Tran. Measuring calibration in deep learning. In IEEE Conf. Comput. Vis. Pattern Recog. Worksh., volume 2(7), 2019

    2019

  286. [295]

    Oktay, E

    O. Oktay, E. Ferrante, K. Kamnitsas, M. Heinrich, W. Bai, J. Caballero, S. Cook, A. Marvao, T. Dawes, D. O'Regan, B. Kainz, B. Glocker, and D. Rueckert. Anatomically Constrained Neural Networks ( ACNN s): Application to cardiac image enhancement and segmentation. IEEE Trans. M...

    2017

  287. [296]

    Oktay, J

    O. Oktay, J. Schlemper, L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N. Hammerla, B. Kainz, B. Glocker, and D. Rueckert. Attention U-Net : Learning where to look for the pancreas. In Conf. Med. Imaging Deep Learn., pages 1--10, 2018

    2018

  288. [297]

    Ouyang, C

    C. Ouyang, C. Chen, S. Li, Z. Li, C. Qin, W. Bai, and D. Rueckert. Causality-inspired single-source domain generalization for medical image segmentation. IEEE Trans. Med. Imag., 42 0 (4): 0 1095--1106, 2022

    2022

  289. [298]

    Painchaud, Y

    N. Painchaud, Y. Skandarani, T. Judge, O. Bernard, A. Lalande, and P. Jodoin. Cardiac segmentation with strong anatomical guarantees. IEEE Trans. Med. Imag., 39 0 (11): 0 3703--13, 2020

    2020

  290. [299]

    J. Pal. Holistic network for quantifying uncertainties in medical images. In International MICCAI brainlesion workshop, pages 560--69. Springer, 2021

    2021

  291. [300]

    Pal and S

    J. Pal and S. Awate. Convex segments for convex objects using DNN boundary tracing and graduated optimization. In Med. Image Comput. Comput.-Assist. Interv., pages 91--101, 2024 a

    2024

Pith tools

Reviewed July 1, 2026 · model on record in the stance chip above.