Pith. sign in

REVIEW 4 major objections 5 minor 44 references

Semantic-Transferable Weakly-Supervised Endoscopic Lesions Segmentation

T0 review · 4 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A semantic-transfer model with class-balanced pseudo-labels and feature-centroid alignment improves weakly-supervised endoscopic lesion segmentation, reaching 63.96 mIoU on a new 3659-image endoscopic dataset.

desk verdict A credible assembly of known domain-adaptation pieces with a useful new endoscopic dataset; single-run results and an unquantified pseudo-label loop make it clearly conditional, but it deserves a real review. read the letter →

arxiv 1908.07669 v2 pith:7T7GQ32C submitted 2019-08-21 cs.CV

Jiahua Dong , Yang Cong , Gan Sun , Dongdong Hou This is my paper · ORCID
classification cs.CV
keywords weaklysupervisedlearningsemanticsegmentationendoscopiclesionpseudo-labelgenerationdomainadaptationfeaturecentroidalignmentadversarialsuperpixelpriors
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

A weakly supervised lesion segmenter that sees only image-level labels on the target endoscopic dataset can be made substantially more accurate by transferring knowledge from a fully labeled source dataset of related gastrointestinal images. The proposed model combines a class-balanced, superpixel-regularized pseudo-label generator with adversarial output alignment and per-class feature-centroid alignment built on exponentially weighted historical features. On the authors' new endoscopic dataset it reports 63.96 mean intersection-over-union (mIoU), versus 61.42 for the closest prior method, and it also improves synthetic-to-real street-scene segmentation, supporting the claim that the transfer mechanism is general rather than task-specific. If correct, the method would let hospitals build usable lesion segmenters from image tags alone, without expensive pixel-level annotation on each target domain.

What carries the argument

The load-bearing mechanism is a self-training loop built from three interacting components. First, a pseudo-label generator ranks all target pixels by predicted class probability, sets a per-class threshold $e^{-\lambda_k}$ so that the top $p$ fraction of confident pixels are selected ($p$ growing from 25% to 55% over training), and then fills unlabeled pixels by voting among spatial neighbors that share the same superpixel region. Second, an adversarial discriminator on segmentation outputs pushes source and target output distributions together. Third, per-class feature centroids are computed from the pseudo-labeled target pixels and the ground-truth source pixels, exponentially averaged over past training iterations, and aligned between domains with an $\ell^2$ plus $\ell^1$ loss. The pseudo-labels do double duty: they supervise target segmentation and define the target centroids.

What would settle it

A direct test: run the pseudo-label generator on images whose lesion masks are known and measure pixel-level precision at the starting 25% threshold and at 55%; if precision is low, or if raising the selected fraction does not improve final mIoU, the self-training loop is not the reason for the reported gains.

Watch

Extended reading notes

Core claim

The paper's central claim is that weakly supervised lesion segmentation can be substantially improved by transferring knowledge from a fully labeled source domain to an image-label-only target domain through a specific combination: class-balanced pseudo-labels refined by superpixel continuity, adversarial alignment of segmentation output distributions, and alignment of per-class feature centroids computed from exponentially weighted historical features. On the authors' collected endoscopic dataset, this reaches 63.96 mean intersection-over-union, against 61.42 for the closest comparison, and the same model reaches 47.2 on synthetic-to-real street-scene transfer versus 44.5 for the best prior method. Ablation results show that removing pseudo-labels, class balance, superpixel refinement, or semantic centroid transfer each lowers performance.

Load-bearing premise

The method assumes that the pixels the model is most confident about are mostly correct, so that building pseudo-labels from the top percent of confident pixels strengthens rather than poisons training.

Editorial extensions

If this is right

  • Image-level labels plus a related fully labeled dataset can train a lesion segmenter that beats prior transfer methods; pixel-level annotation of the target domain is not required.
  • The same pipeline transfers from synthetic street scenes to real street scenes, so it is not tied to gastrointestinal endoscopy.
  • Expanding the selected confident-pixel fraction during training gradually adds harder target samples, so segmentation quality should improve as training progresses rather than plateauing early.
  • Class-balanced thresholds and superpixel voting specifically protect rare and spatially scattered classes, so the method's main gains should appear on hard-to-transfer categories.

Reading between the lines

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

  • Editorial inference: because pixel-annotated source images are available, the pseudo-label generator could be validated directly on them; the paper does not report this, and that measurement would separate pseudo-label quality from the domain-alignment losses.
  • Editorial inference: a natural extension is to make per-class thresholds depend on class prevalence instead of a shared percentile schedule, which should improve rare-class recall if the class-balance assumption holds.
  • Editorial inference: an oracle experiment that replaces target pseudo-labels with human pixel labels on a small subset would quantify the ceiling of the self-training loop, something the paper leaves implicit.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes a weakly-supervised semantic segmentation method for endoscopic lesions. The method trains on a fully-labeled source domain (gastroscope images) and an image-level-labeled target domain (enteroscopy images), combining three main components: a pseudo-label generator with class-balanced confidence thresholds and superpixel spatial refinement; adversarial alignment of segmentation outputs across domains; and a semantic representation transfer loss that aligns exponentially weighted feature centroids for each class. The model is evaluated on a newly collected endoscopic dataset and on two standard synthetic-to-real benchmarks (SYNTHIA to Cityscapes, GTA to Cityscapes), with ablation studies for each component. The central claim is that the combination of these components improves weakly-supervised target-domain segmentation compared with prior domain adaptation and weakly supervised segmentation methods.

Significance. If the reported gains are reliable, the paper offers a practically useful direction: it reduces the need for pixel-level annotations in endoscopy by transferring knowledge from a related fully-labeled source domain. The release of a new endoscopic dataset is itself a valuable contribution, and the ablations across three transfer settings provide a systematic view of the method's components. However, the empirical support is currently weakened by the complete absence of error bars, significance tests, or repeated-run statistics, and by the fact that the pseudo-label generator—the sole source of pixel-level target supervision—is never validated for label accuracy or calibration. The central claim is plausible but is not yet established at the standard needed for a strong journal publication.

major comments (4)
  1. [Section 4, Tables 1–4] All reported results appear to come from a single training run: no error bars, standard deviations, or significance tests are provided anywhere. This matters because several key differences are small in absolute terms (e.g., Table 1: 63.96 vs. 61.42 mIoU; Table 2: Ours 63.96 vs. BL+PL+SRT 63.58; Table 3: Ours 42.9 vs. Ours-woSP 42.3). Without repeated runs and variance estimates, the claimed improvements over the nearest baselines cannot be distinguished from run-to-run noise. I request the authors to report means and standard deviations over at least three seeds, and, where possible, a paired significance test for the main comparisons.
  2. [Section 3.2, Algorithm 1, Eq. (5), Algorithm 3] The pseudo-label generator is the only source of pixel-level supervision on the target domain, but its output quality is never measured. Algorithm 1 sets class thresholds at the (1-p) quantile of the model's softmax confidence, and Eq. (5) assigns a label to every pixel whose confidence exceeds the threshold. These are confidence thresholds, not accuracy thresholds; under domain shift, a model's softmax scores may be miscalibrated, and the selected top-p% pixels may contain a substantial share of confident errors. Because Eq. (3) trains the segmenter directly on these labels and Algorithm 3 computes target centroids from them, systematic errors (e.g., predicting only the easy central region of a lesion) are self-reinforced. The ablation Ours-woPL shows that adding pseudo-labels improves mIoU, but it does not distinguish correct pseudo-labels from merely adding target pixels to the segmentation loss. I request a pseudo-label error analysis: report per-class precision and recall of pseudo-labels against a small manually labeled target subset as a function of p, and include calibration curves for the target domain.
  3. [Section 3.2, Algorithm 3, lines 9–10] The exponentially weighted centroid update is written as C_k = sum_{x=1}^n C_x * gamma^{n-x} with gamma = 0.7. As written, this is an unnormalized cumulative sum, so the magnitude of the centroid grows with the iteration count n and the quantity is not a moving average; this could make the LSRT loss unstable as training proceeds. The authors should clarify whether the weights are normalized (e.g., divided by sum of gamma^{n-x}) and how centroids are initialized or handled for classes that do not appear in a given batch.
  4. [Section 4.1, Medical Endoscopic Dataset] The dataset description states that 3659 images were collected from more than 1100 volunteers, but it does not state whether the 390 test enteroscopy images and the 300 training enteroscopy images come from disjoint volunteers. If images from the same patient appear in both training and test sets, the reported medical segmentation performance could be inflated by patient-level leakage. The authors should confirm that the train/test split is patient-disjoint and, ideally, report how many volunteers are used in each split.
minor comments (5)
  1. [Section 3.2, Eqs. (3) and (4)] Equation (3) uses a scalar weight lambda in the l1-norm term, while Eq. (4) and Algorithm 1 use class-dependent lambda_k; the relationship between the two formulations should be clarified.
  2. [Section 4.2, Figure 5] The hyperparameter sensitivity plots in Figure 5 are difficult to read from the printed text; the authors should provide the numeric values or ensure the curves and axis labels are legible.
  3. [Throughout] There are several typos, including 'previously-leaned experience' in Section 3.2 and 'gasteroscope' in the caption of Figure 3; a careful proofread is needed.
  4. [References] Reference [11] is cited as 'CVPR, 2019' for the ImageNet paper; the correct venue is CVPR 2009.
  5. [Section 4.2, Ablation Study] The text states that removing pseudo-label selection or semantic representation transfer decreases mIoU by 0.38% to 4.42%, but Table 2 shows several ablations within this range; please state explicitly which pairs the 0.38% and 4.42% refer to.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the model uses self-training pseudo-labels, but the central claim is judged on held-out manually annotated test images and external benchmark splits, so no prediction reduces to a fitted input by construction.

full rationale

This paper is an empirical study rather than a derivation, and I find no step in which a claimed prediction is equivalent, by the paper's own equations or by self-citation, to its inputs. The pseudo-label generator (Algorithm 1, Eq. 5, Algorithm 2) selects target pixels by confidence thresholds and superpixel voting, and Eq. (3) then trains the segmentation subnet on those pseudo-labels. This is a self-training loop: the model supervises itself from its own softmax outputs. Self-training is self-referential by design, but it is not circular in the sense defined here because the paper does not claim to derive the pseudo-labels' accuracy from first principles, and its effectiveness claim is grounded externally. The medical dataset evaluation uses 390 held-out enteroscopy images with manual annotations, disjoint from the 300 image-level-labeled training images, and the benchmark experiments (SYNTHIA/Cityscapes, GTA/Cityscapes) evaluate on the standard Cityscapes validation subset that is disjoint from the training split. The ablations (Ours-woPL, Ours-woCB, Ours-woSP) compare variants on these same external test sets, so the reported improvements are not forced by construction. The feature-centroid alignment and adversarial losses are optimization objectives, not predictions that reduce to fitted parameters. The paper's citations to prior work by the same authors ([7], [37]) appear only as related-work context for endoscopic CAD and are not load-bearing for the proposed model. The unquantified reliability of the confidence-based pseudo-labels is a legitimate correctness concern, but the hard rules require exhibiting a specific reduction of a claimed result to its inputs; no such reduction is present here. Accordingly, the appropriate finding is no significant circularity, score 0.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claims rest on standard deep learning assumptions plus several domain assumptions about the transfer task and the reliability of pseudo-labels. Hyperparameters mu, eta, alpha, gamma and the pseudo-label selection schedule are tuned empirically.

free parameters (5)
  • mu = 10
    Weight for semantic representation transfer loss LSRT in Eq. (1); tuned empirically in Fig. 5b.
  • eta = 0.3
    Weight for adversarial loss LD in Eq. (1); tuned empirically in Fig. 5b.
  • alpha = 1
    Weight for the L1 term in centroid loss Eq. (7); tuned with gamma in Fig. 5.
  • gamma = 0.7
    Exponential decay factor for previously learned features in Algorithm 3; tuned in Fig. 5.
  • p schedule = 25% to 55%, increment 5% per epoch
    Portion of confident pixels selected per class in Algorithm 1; empirically chosen, stated in Section 3.2.
assumptions (4)
  • domain assumption Source and target datasets share the same label space and similar appearance distributions (e.g., gastroscope and enteroscopy both depict GI lesions with similar visual properties).
    Section 1: 'diseases detected by both gastroscope and enteroscopy tend to share similar appearances, and further have similar prior distributions.' This underpins the entire transfer model.
  • domain assumption Per-pixel softmax confidence is a reliable proxy for pseudo-label correctness, so selecting the top p% confident pixels per class yields accurate training labels.
    Algorithm 1 and Eq. (5) set thresholds based on confidence quantiles; if confidence is miscalibrated, self-training can reinforce errors. No calibration analysis is provided.
  • domain assumption Superpixel spatial priors improve pseudo-label continuity: pixels in a superpixel with 8-neighborhood agreement can be safely assigned the majority label.
    Algorithm 2 fills unlabeled pixels by voting among neighbors with the same superpixel prior; assumes lesions are spatially coherent within superpixels.
  • domain assumption A DeepLab-v3/ResNet-50 backbone pretrained on ImageNet provides a suitable feature extractor for endoscopic images.
    Used as the network backbone in Section 3.3; standard transfer-learning assumption that general features help medical images.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Semantic-Transferable Weakly-Supervised Endoscopic Lesions Segmentation." pith.science (2026). https://pith.science/paper/7T7GQ32C

@misc{pith2026190807669,
  author       = {Pith},
  title        = {Pith review of: Semantic-Transferable Weakly-Supervised Endoscopic Lesions Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7T7GQ32C}},
  note         = {Machine review of arXiv:1908.07669}
}
read the original abstract

Weakly-supervised learning under image-level labels supervision has been widely applied to semantic segmentation of medical lesions regions. However, 1) most existing models rely on effective constraints to explore the internal representation of lesions, which only produces inaccurate and coarse lesions regions; 2) they ignore the strong probabilistic dependencies between target lesions dataset (e.g., enteroscopy images) and well-to-annotated source diseases dataset (e.g., gastroscope images). To better utilize these dependencies, we present a new semantic lesions representation transfer model for weakly-supervised endoscopic lesions segmentation, which can exploit useful knowledge from relevant fully-labeled diseases segmentation task to enhance the performance of target weakly-labeled lesions segmentation task. More specifically, a pseudo label generator is proposed to leverage seed information to generate highly-confident pseudo pixel labels by incorporating class balance and super-pixel spatial prior. It can iteratively include more hard-to-transfer samples from weakly-labeled target dataset into training set. Afterwards, dynamically searched feature centroids for same class among different datasets are aligned by accumulating previously-learned features. Meanwhile, adversarial learning is also employed in this paper, to narrow the gap between the lesions among different datasets in output space. Finally, we build a new medical endoscopic dataset with 3659 images collected from more than 1100 volunteers. Extensive experiments on our collected dataset and several benchmark datasets validate the effectiveness of our model.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

44 extracted references · 39 canonical work pages

  1. [1]

    Slic superpix- els compared to state-of-the-art superpixel methods

    Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Susstrunk. Slic superpix- els compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11):2274–2282, Nov. 2012. 2, 4

    work page 2012

  2. [2]

    Unsupervised Pixel- Level Domain Adaptation with Generative Adversarial Net- works

    Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, and Dilip Krishnan. Unsupervised Pixel- Level Domain Adaptation with Generative Adversarial Net- works. In In CVPR, 2017. 2

    work page 2017

  3. [3]

    Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.IEEE Transactions on Pattern Analysis and Machine Intelligence , 40(4):834– 848, Apr. 2018. 5

    work page 2018

  4. [4]

    Encoder-decoder with atrous separable convolution for semantic image segmentation

    Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. Encoder-decoder with atrous separable convolution for semantic image segmentation. In ECCV, 2018. 2, 5, 6, 7

    work page 2018

  5. [5]

    No More Discrimina- tion: Cross City Adaptation of Road Scene Segmenters

    Yi-Hsin Chen, Wei-Yu Chen, Yu-Ting Chen, Bo-Cheng Tsai, Yu-Chiang Frank Wang, and Min Sun. No More Discrimina- tion: Cross City Adaptation of Road Scene Segmenters. In ICCV, Oct 2017. 2, 6, 8

    work page 2017

  6. [6]

    Computer-aided US Diagnosis of Breast Le- sions by Using Cell-based Contour Grouping

    Jie-Zhi Cheng, Yi-Hong Chou, Chiun-Sheng Huang, Yeun- Chung Chang, Chui-Mei Tiu, Kuei-Wu Chen, and Chung- Ming Chen. Computer-aided US Diagnosis of Breast Le- sions by Using Cell-based Contour Grouping. Radiology, 255(3):746–754, June 2010. 2

    work page 2010

  7. [7]

    Deep sparse feature selection for computer aided endoscopy diagnosis

    Yang Cong, Shuai Wang, Ji Liu, Jun Cao, Yunsheng Yang, and Jiebo Luo. Deep sparse feature selection for computer aided endoscopy diagnosis. Pattern Recognition, 48(3):907 – 917, 2015. 2

    work page 2015

  8. [8]

    The cityscapes dataset for semantic urban scene understanding

    Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In CVPR,

Show all 44 references
  1. [9]

    Simultaneous estimation and segmentation from projection data in dynamic pet

    Jianan Cui, Haiqing Yu, Shuhang Chen, Yunmei Chen, and Huafeng Liu. Simultaneous estimation and segmentation from projection data in dynamic pet. Medical physics , 46(3):1245–1259, 2019. 2

    2019

  2. [10]

    A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation

    Avijit Dasgupta and Sonam Singh. A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation. In 2017 IEEE 14th Interna- tional Symposium on Biomedical Imaging (ISBI 2017), pages 248–251, Melbourne, Australia, Apr. 2017. IEEE. 2

    2017

  3. [11]

    ImageNet: A Large-Scale Hierarchical Image Database

    Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A Large-Scale Hierarchical Image Database. In CVPR, 2019. 5

    2019

  4. [12]

    Unsupervised cross-modality domain adaptation of convnets for biomedical image segmentations with adver- sarial loss

    Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, and Pheng- Ann Heng. Unsupervised cross-modality domain adaptation of convnets for biomedical image segmentations with adver- sarial loss. In Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI’18,...

  5. [13]

    Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio

    Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, NIPS’14, pa...

    2014

  6. [14]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR ,

  7. [15]

    CyCADA: Cycle-consistent adversarial domain adapta- tion

    Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, and Trevor Dar- rell. CyCADA: Cycle-consistent adversarial domain adapta- tion. In Proceedings of the 35th International Conference on Machine Learning , volume 80 of Proceedings of ...

    1989

  8. [16]

    Fcns in the wild: Pixel-level adversarial and constraint-based adaptation

    Judy Hoffman, Dequan Wang, Fisher Yu, and Trevor Darrell. Fcns in the wild: Pixel-level adversarial and constraint-based adaptation. arXiv:1612.02649, 2016. 2, 6, 7, 8

    2016 arXiv

  9. [17]

    Conditional generative adversarial network for struc- tured domain adaptation

    Weixiang Hong, Zhenzhen Wang, Ming Yang, and Junsong Yuan. Conditional generative adversarial network for struc- tured domain adaptation. In CVPR, June 2018. 2, 6, 7, 8

    2018

  10. [18]

    Giger, Luz A

    Karla Horsch, Maryellen L. Giger, Luz A. Venta, and Carl J. Vyborny. Automatic segmentation of breast lesions on ultra- sound. Medical Physics, 28(8):1652–1659, Aug. 2001. 2

    2001

  11. [19]

    Constrained Deep Weak Supervision for Histopathol- ogy Image Segmentation

    Zhipeng Jia, Xingyi Huang, Eric I.-Chao Chang, and Yan Xu. Constrained Deep Weak Supervision for Histopathol- ogy Image Segmentation. IEEE Transactions on Medical Imaging, 36(11):2376–2388, Nov. 2017. 1, 2, 6

    2017

  12. [20]

    Reti- nal blood vessel segmentation using fully convolutional net- work with transfer learning

    Zhexin Jiang, Hao Zhang, Yi Wang, and Seok-Bum Ko. Reti- nal blood vessel segmentation using fully convolutional net- work with transfer learning. Computerized Medical Imaging and Graphics, 68:1–15, Sept. 2018. 2

    2018

  13. [21]

    Self- paced learning for latent variable models

    M Pawan Kumar, Benjamin Packer, and Daphne Koller. Self- paced learning for latent variable models. In Advances in Neural Information Processing Systems , pages 1189–1197,

  14. [22]

    Lillicrap, Jonathan J

    Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. Continuous control with deep reinforcement learning. In ICLR, 2016. 4

    2016

  15. [23]

    Mingsheng Long, Yue Cao, Jianmin Wang, and Michael I. Jordan. Learning Transferable Features with Deep Adapta- tion Networks. In ICML, Feb. 2015. 2

    2015

  16. [24]

    Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I. Jordan. Unsupervised domain adaptation with residual transfer networks. In Proceedings of the 30th Interna- tional Conference on Neural Information Processing Sys- tems, NIPS’16, pages 136–144, USA, 2016. Curran Asso- ciates Inc. 2

    2016

  17. [25]

    Riedmiller

    V olodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin A. Riedmiller. Playing atari with deep reinforcement learning. In NIPS, 2013. 4

    2013

  18. [26]

    Unsuper- vised representation learning with deep convolutional gener- ative adversarial networks

    Alec Radford, Luke Metz, and Soumith Chintala. Unsuper- vised representation learning with deep convolutional gener- ative adversarial networks. arXiv:1511.06434, 2015. 5

    2015 arXiv

  19. [27]

    Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun

    Stephan R. Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun. Playing for data: Ground truth from computer games. In ECCV, 2016. 6, 8

    2016

  20. [28]

    U-net: Convolutional networks for biomedical image segmentation

    Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Nassir Navab, Joachim Hornegger, William M. Wells, and Alejandro F. Frangi, editors, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015...

    2015

  21. [29]

    German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, and Antonio M. Lopez. The synthia dataset: A large collection of synthetic images for semantic segmenta- tion of urban scenes. In CVPR, June 2016. 6, 7

    2016

  22. [30]

    Learning from synthetic data: Addressing domain shift for semantic segmentation

    Swami Sankaranarayanan, Yogesh Balaji, Arpit Jain, Ser- Nam Lim, and Rama Chellappa. Learning from synthetic data: Addressing domain shift for semantic segmentation. In CVPR, June 2018. 7, 8

    2018

  23. [31]

    Computer-Aided Diagnosis and Artificial Intelli- gence in Clinical Imaging

    Junji Shiraishi, Qiang Li, Daniel Appelbaum, and Ku- nio Doi. Computer-Aided Diagnosis and Artificial Intelli- gence in Clinical Imaging. Seminars in Nuclear Medicine , 41(6):449–462, Nov. 2011. 2

    2011

  24. [32]

    Very deep con- volutional networks for large-scale image recognition

    Karen Simonyan and Andrew Zisserman. Very deep con- volutional networks for large-scale image recognition. In CVPR, 2014. 2

    2014

  25. [33]

    Learning to adapt structured output space for semantic seg- mentation

    Yi-Hsuan Tsai, Wei-Chih Hung, Samuel Schulter, Ki- hyuk Sohn, Ming-Hsuan Yang, and Manmohan Chandraker. Learning to adapt structured output space for semantic seg- mentation. In CVPR, 2018. 2, 6, 7, 8

    2018

  26. [34]

    Simultaneous deep transfer across domains and tasks

    Eric Tzeng, Judy Hoffman, Trevor Darrell, and Kate Saenko. Simultaneous deep transfer across domains and tasks. In ICCV, 2015. 2

    2015

  27. [35]

    Adversarial discriminative domain adaptation

    Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Dar- rell. Adversarial discriminative domain adaptation. In ICCV,

  28. [36]

    Visualizing data using t-SNE

    Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-SNE. Journal of Machine Learning Research , 9:2579–2605, 2008. 7

    2008

  29. [37]

    Computer-aided endoscopic diagnosis without human-specific labeling

    Shuai Wang, Yang Cong, Huijie Fan, Lianqing Liu, Xiao- qiu Li, Yunsheng Yang, Yangdong Tang, Huaici Zhao, and Haibin Yu. Computer-aided endoscopic diagnosis without human-specific labeling. IEEE Transactions on Biomedical Engineering, 63(11):2347–2358, Nov 2016. 2

    2016

  30. [38]

    Weakly supervised histopathology cancer im- age segmentation and classification

    Yan Xu, Jun-Yan Zhu, Eric I-Chao Chang, Maode Lai, and Zhuowen Tu. Weakly supervised histopathology cancer im- age segmentation and classification. Medical Image Analy- sis, 18(3):591–604, Apr. 2014. 1, 2

    2014

  31. [39]

    Weakly supervised deep learning for thoracic disease classification and localization on chest x-rays

    Chaochao Yan, Jiawen Yao, Ruoyu Li, Zheng Xu, and Jun- zhou Huang. Weakly supervised deep learning for thoracic disease classification and localization on chest x-rays. In Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health ...

    2018

  32. [40]

    Learning a discriminative feature network for semantic segmentation

    Changqian Yu, Jingbo Wang, Chao Peng, Changxin Gao, Gang Yu, and Nong Sang. Learning a discriminative feature network for semantic segmentation. CoRR, abs/1804.09337,

    arXiv

  33. [41]

    Multi-scale context aggrega- tion by dilated convolutions

    Fisher Yu and Vladlen Koltun. Multi-scale context aggrega- tion by dilated convolutions. In ICLR, 2016. 8

    2016

  34. [42]

    Curricu- lum domain adaptation for semantic segmentation of urban scenes

    Yang Zhang, Philip David, and Boqing Gong. Curricu- lum domain adaptation for semantic segmentation of urban scenes. In ICCV, Oct 2017. 2, 7, 8

    2017

  35. [43]

    Weakly-supervised simultaneous evidence identification and segmentation for automated glaucoma di- agnosis

    Rongchang Zhao, Wangmin Liao, Beiji Zou, Zailiang Chen, and Shuo Li. Weakly-supervised simultaneous evidence identification and segmentation for automated glaucoma di- agnosis. 2019. 1

    2019

  36. [44]

    Vijaya Kumar, and Jinsong Wang

    Yang Zou, Zhiding Yu, B.V .K. Vijaya Kumar, and Jinsong Wang. Unsupervised domain adaptation for semantic seg- mentation via class-balanced self-training. In The European Conference on Computer Vision (ECCV), September 2018. 2

    2018

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.