LesiOnTime -- Joint Temporal and Clinical Modeling for Small Breast Lesion Segmentation in Longitudinal DCE-MRI

Christian F. Singer; Georg Langs; Maria Bernathova; Mohammed Kamran; Philipp Seeb\"ock; Raoul Varga; Thomas Helbich; Zsuzsanna Bago-Horvath

REVIEW 4 major objections 3 minor 1 cited by

LesiOnTime -- Joint Temporal and Clinical Modeling for Small Breast Lesion Segmentation in Longitudinal DCE-MRI

T0 review · 4 major / 3 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Prior scans and BI-RADS scores lift lesion segmentation by 5%

desk verdict The abstract describes a sensible longitudinal DCE-MRI segmentation method, but the submitted full text is a different paper on prosthetic hand grasping, so the claimed 5% Dice improvement is completely unverifiable as submitted. read the letter →

arxiv 2508.00496 v2 pith:6ZMZCXCA submitted 2025-08-01 cs.CV cs.AI

Mohammed Kamran , Maria Bernathova , Raoul Varga , Christian F. Singer , Zsuzsanna Bago-Horvath , Thomas Helbich , Georg Langs , Philipp Seeböck This is my paper

classification cs.CVcs.AI

keywords breastcancerscreeningDCE-MRIlesionsegmentationtemporalpriorattentionBI-RADSscorelongitudinalimaging3Dclinicalcontext

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

LesiOnTime claims that the accuracy ceiling for segmenting small breast lesions in DCE-MRI is set not by image resolution but by the model's ability to use the two cues radiologists rely on: the patient's previous scans and the BI-RADS assessment recorded at each timepoint. The paper introduces a 3D segmentation network with a Temporal Prior Attention block that lets current-scan features query prior-scan features, and a BI-RADS Consistency Regularization loss that pulls latent representations of scans sharing the same BI-RADS category closer together. On an in-house longitudinal dataset of high-risk patients, this combination is reported to outperform single-timepoint and other longitudinal baselines by 5% Dice, with ablations showing both components contribute. The intended upshot is that routine clinical metadata, which is already collected in screening, can be converted into a training signal that improves early lesion detection.

What carries the argument

The two load-bearing mechanisms are the Temporal Prior Attention (TPA) block and the BI-RADS Consistency Regularization (BCR) loss. TPA is an attention module that takes features from the current DCE-MRI scan and from earlier scans and computes a dynamic integration of the two, so the network can decide what prior information matters for the current lesion boundary. BCR is an auxiliary loss that pushes latent feature vectors of scans with the same BI-RADS score toward each other during training, injecting the clinical scoring system as a geometric constraint on the representation. The paper's argument is that these two mechanisms, not a larger backbone or more training data, are what close the gap for small lesions.

What would settle it

Re-run the same training and evaluation protocol on a public longitudinal DCE-MRI dataset, but with the BI-RADS labels randomly shuffled during training; if the shuffled-label model still beats the single-timepoint baseline by roughly 5% Dice, the reported gain is not caused by clinical meaning. A second check is to re-score the validation scans with an independent radiologist and confirm the gain persists when only cases with inter-reader agreement are kept.

Watch

Extended reading notes

Core claim

The central discovery is that temporal and clinical context are not just useful heuristics but can be made into differentiable network components that directly improve small-lesion segmentation. The Temporal Prior Attention block dynamically weights and integrates features from earlier scans when segmenting the current one, mimicking the radiologist's side-by-side comparison. The BI-RADS Consistency Regularization loss treats the radiological assessment as a structured label: scans with the same BI-RADS category should occupy nearby regions of the learned latent space. Together, the two mechanisms are credited with a 5% Dice gain over state-of-the-art single-timepoint and longitudinal baselines on the curated in-house dataset, and the ablations attribute complementary gains to each term.

Load-bearing premise

The paper's gain depends on BI-RADS scores being reliable and consistently assigned, because the BCR loss treats them as trustworthy grouping labels; if the scores are noisy or radiologists disagree, the alignment could teach the network spurious structure and the 5% Dice improvement could vanish.

Editorial extensions

If this is right

If the 5% Dice gain reproduces, screening systems can improve segmentation of subtle lesions without any new annotation burden, because prior scans and BI-RADS scores are already recorded in routine follow-up.
The TPA block suggests a general recipe: temporal fusion can be posed as attention between timepoints and trained end-to-end, which could extend to other longitudinal imaging tasks such as tumor growth tracking or multiple-sclerosis lesion monitoring.
The BCR loss offers a template for injecting structured clinical labels into segmentation training; any ordinal or categorical scoring system with known clinical meaning could be used the same way.
Ablations showing complementary gains imply that a clinician-informed training signal and a temporal-fusion mechanism address different sources of error; combining them may be better than either alone.

Reading between the lines

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

A natural test would be to shuffle the BI-RADS labels during training; if the BCR loss with random labels gives a similar Dice gain, the reported improvement is coming from a generic regularization effect, not from clinical meaning.
Because the dataset is in-house, the 5% figure is not yet benchmarked against public longitudinal breast MRI datasets; reproducing it there would separate a method-level advantage from dataset-specific cues.
If inter-reader variability of BI-RADS is a concern, weighting the consistency loss by radiologist confidence or adjudicated labels might make the gain more robust; the paper does not explore this.
The TPA block's dynamic integration could also be read as a learned attention that highlights changes between timepoints; a visualization study of the attention maps might reveal whether the network is detecting growth or new enhancement, which radiologists could audit.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Two things to know about arXiv:2508.00496. The abstract describes a sensible idea: a 3D segmentation network for small breast lesions in longitudinal DCE-MRI, with a Temporal Prior Attention block and a BI-RADS Consistency Regularization loss, reporting a 5% Dice gain over single-timepoint and longitudinal baselines on an in-house high-risk screening dataset. The full text of the submission, however, is not that paper. It is a completely unrelated manuscript about imitation learning for the Hannes prosthetic hand. Nothing in the supplied body describes LesiOnTime, its architecture, training, dataset, or evaluation. So the central claim hangs on an abstract alone. What is genuinely new here, taking the abstract at face value, is the combination of temporal attention with a clinical-score consistency loss for longitudinal breast MRI. Each component exists elsewhere, but the pairing is not standard, and small-lesion segmentation in screening is a clinically relevant gap. If the 5% gain is real and reproducible, it matters to the medical imaging community. The soft spot is not subtle. As submitted, there is no evidence to inspect. The comparison, the ablations, the error bars, the handling of BI-RADS noise—none of it exists in this manuscript. Even after fixing the body, I would want to know two things: whether BI-RADS scores are available at inference or only during training, and what the inter-rater agreement is on the scores used to supervise the consistency loss. The abstract does not say. That is a legitimate methodological concern, but it is secondary to the fact that the paper as submitted is not the paper. This paper is for a reader interested in longitudinal medical imaging and clinically conditioned segmentation, but only in a corrected version. The current submission would not survive desk screening, and it shouldn't. My recommendation: send it back to the authors to fix the manuscript mismatch. Once the correct body is available, ask for a statistical significance test and an external validation before taking the 5% claim seriously.

Referee Report

4 major / 3 minor

Summary. The abstract of arXiv:2508.00496 announces LesiOnTime, a 3D segmentation method for small breast lesions in longitudinal DCE-MRI that combines a Temporal Prior Attention (TPA) block with a BI-RADS Consistency Regularization (BCR) loss, and reports a 5% Dice improvement over single-timepoint and longitudinal baselines on a curated in-house dataset of high-risk patients. However, the submitted full text is not the LesiOnTime paper: it is the complete text of "HannesImitation: Grasping with the Hannes Prosthetic Hand via Imitation Learning" (arXiv:2508.00491). Consequently, the submission contains no description of the LesiOnTime method, no dataset details, no baseline definitions, no experimental tables, and no ablation studies. The only available evidence for the central claim is the abstract itself, which reports a single percentage gain without error bars, significance tests, or external validation.

Significance. If the claimed results were properly documented and validated, the proposed idea would be clinically relevant: incorporating longitudinal imaging and BI-RADS clinical scores into lesion segmentation addresses a real gap in screening workflows, and the two proposed components (TPA and BCR) are plausible and complementary. The promise of public code is also a strength. However, because the submitted manuscript does not contain the LesiOnTime text at all, none of these contributions can be inspected, checked, or placed in context. The significance of the work is therefore entirely conditional on the existence of a correct full text that is not present in this submission.

major comments (4)

[Full text] The body of arXiv:2508.00496 is the paper "HannesImitation: Grasping with the Hannes Prosthetic Hand via Imitation Learning", not the LesiOnTime manuscript. None of the central elements of the claimed contribution appear in the submission: the Temporal Prior Attention block, the BI-RADS Consistency Regularization loss, the longitudinal DCE-MRI dataset, the baseline definitions, the evaluation protocol, or the ablation studies. The abstract's claim of a 5% Dice improvement is therefore completely unverifiable from the submitted text, and the manuscript as it stands cannot be assessed for correctness or reproducibility.
[Abstract] The abstract does not state whether BI-RADS scores are required at inference time or are used only during training. The BCR loss enforces latent-space alignment for scans with similar radiological assessments, so the reliability of the BI-RADS labels is load-bearing for the claimed gains. The authors should report inter-rater agreement or otherwise validate label consistency; without this, the regularization could encode label noise and the reported 5% improvement could be an artifact. This concern can only be properly examined once the actual method description is present.
[Abstract] The reported improvement is given as a single percentage point (5% Dice) with no error bars, no number of patients or scans, no cross-validation scheme, and no statistical significance test. Even with the correct manuscript, a single aggregate number without variance or significance reporting would be insufficient to support the claim that LesiOnTime outperforms state-of-the-art baselines, especially for small lesion segmentation where Dice scores can vary substantially across cases.
[Full text] The reference list and related-work discussion belong to the HannesImitation paper on prosthetic-hand imitation learning, so the submission provides no context for the claimed longitudinal DCE-MRI segmentation comparison. No prior single-timepoint or longitudinal breast lesion segmentation methods are cited, and no BI-RADS-based modeling approaches are discussed; thus the state-of-the-art comparison mentioned in the abstract cannot be placed in the literature.

minor comments (3)

[Abstract] The abstract mentions a public repository (https://github.com/cirmuw/LesiOnTime), but the submission contains no code, no reproducibility checklist, and no verification that the repository corresponds to the described method; please confirm the repository is accessible and properly linked.
[Metadata] The arXiv submission's abstract and full text describe two completely different papers with different titles, authors, and subject areas; the submission should be corrected or withdrawn so that the abstract and body refer to the same work.
[Abstract] The phrasing "outperforms state-of-the-art ... by 5% in terms of Dice" is ambiguous; it should specify the exact Dice variant (e.g., whole-volume vs. per-lesion, lesion-wise vs. scan-wise) and enumerate the baselines included in the comparison.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity present: the LesiOnTime abstract is unverifiable because the submitted full text is an unrelated robotics paper, but no derivation reduces to its own inputs.

full rationale

The circularity pass requires exhibiting a specific reduction: a fitted parameter renamed as a prediction, an ansatz smuggled in via self-citation, or an equation that equals its own input by construction. The submitted manuscript does not provide any of these for LesiOnTime. The abstract claims that a Temporal Prior Attention block and a BI-RADS Consistency Regularization loss yield a 5% Dice improvement, but the full text is a different paper, 'HannesImitation: Grasping with the Hannes Prosthetic Hand via Imitation Learning' (arXiv:2508.00491). There are therefore no LesiOnTime equations, experimental tables, baseline definitions, or ablation results to analyze. This is an evidential mismatch, not a circular argument. The only described inputs to the claimed method—previous scans of the same patient and BI-RADS scores—are external clinical and imaging inputs, not outputs of the segmentation model, so no self-referential loop can be identified even from the abstract's description. No self-citation is load-bearing, no uniqueness theorem is imported from the authors, and no known result is renamed. Because the hard rules require quoting a specific circular step and none can be exhibited, the appropriate finding is 'no significant circularity' with score 0.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The abstract introduces no invented entities. The central claim depends on the reliability of BI-RADS labels, the comparability of longitudinal scans, and the representativeness of the in-house dataset, none of which is demonstrated in the abstract. The BCR loss weight is a likely free hyperparameter.

free parameters (1)

BI-RADS consistency regularization weight = not reported in abstract
The balance between the BI-RADS consistency loss and the segmentation loss is a hyperparameter that likely affects the reported 5% Dice gain. The abstract does not provide its value or a sensitivity analysis.

assumptions (3)

domain assumption BI-RADS scores are reliable and consistently assigned across radiologists and timepoints.
The BCR loss aligns latent representations for scans with similar BI-RADS assessments; noisy or inconsistent labels would inject spurious structure into training.
domain assumption Longitudinal DCE-MRI scans can be meaningfully aligned or compared across timepoints.
The TPA block integrates previous and current scans; if scans are not co-registered or differ in protocol, the temporal prior could mislead segmentation.
domain assumption The in-house dataset is representative of the target high-risk screening population.
The 5% Dice improvement is measured on one curated dataset; generalization to other scanners and patient populations is unverified.

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Cite this review

Pith. "Pith review of LesiOnTime -- Joint Temporal and Clinical Modeling for Small Breast Lesion Segmentation in Longitudinal DCE-MRI." pith.science (2026). https://pith.science/paper/6ZMZCXCA

@misc{pith2026250800496,
  author       = {Pith},
  title        = {Pith review of: LesiOnTime -- Joint Temporal and Clinical Modeling for Small Breast Lesion Segmentation in Longitudinal DCE-MRI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6ZMZCXCA}},
  note         = {Machine review of arXiv:2508.00496}
}

read the original abstract

Accurate segmentation of small lesions in Breast Dynamic Contrast-Enhanced MRI (DCE-MRI) is critical for early cancer detection, especially in high-risk patients. While recent deep learning methods have advanced lesion segmentation, they primarily target large lesions and neglect valuable longitudinal and clinical information routinely used by radiologists. In real-world screening, detecting subtle or emerging lesions requires radiologists to compare across timepoints and consider previous radiology assessments, such as the BI-RADS score. We propose LesiOnTime, a novel 3D segmentation approach that mimics clinical diagnostic workflows by jointly leveraging longitudinal imaging and BIRADS scores. The key components are: (1) a Temporal Prior Attention (TPA) block that dynamically integrates information from previous and current scans; and (2) a BI-RADS Consistency Regularization (BCR) loss that enforces latent space alignment for scans with similar radiological assessments, thus embedding domain knowledge into the training process. Evaluated on a curated in-house longitudinal dataset of high-risk patients with DCE-MRI, our approach outperforms state-of-the-art single-timepoint and longitudinal baselines by 5% in terms of Dice. Ablation studies demonstrate that both TPA and BCR contribute complementary performance gains. These results highlight the importance of incorporating temporal and clinical context for reliable early lesion segmentation in real-world breast cancer screening. Our code is publicly available at https://github.com/cirmuw/LesiOnTime

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Reference graph

Works this paper leans on

22 extracted references · 18 canonical work pages · cited by 1 Pith paper

[1]

Website, American Cancer Society (2025), https://www.cancer.org/cancer/types/breast-cancer.html, accessed 2025‑06‑12

American Cancer Society : Breast cancer. Website, American Cancer Society (2025), https://www.cancer.org/cancer/types/breast-cancer.html, accessed 2025‑06‑12

work page 2025
[2]

European Radiology Experimental 7(1), 32 (2023)

Burger, B., Bernathova, M., Seeb \"o ck, P., Singer, C.F., Helbich, T.H., Langs, G.: Deep learning for predicting future lesion emergence in high-risk breast mri screening: a feasibility study. European Radiology Experimental 7(1), 32 (2023)

work page 2023
[3]

In: 2021 IEEE symposium on computers and communications (ISCC)

Carvalho, E.D., Silva, R.R.V., Mathew, M.J., Araujo, F.H.D., De Carvalho Filho, A.O.: Tumor segmentation in breast dce-mri slice using deep learning methods. In: 2021 IEEE symposium on computers and communications (ISCC). pp. 1--6. IEEE (2021)

work page 2021
[4]

the cancer imaging archive (2024)

Daniels, D., Last, D., Cohen, K., Mardor, Y., Sklair-Levy, M.: Standard and delayed contrast-enhanced mri of malignant and benign breast lesions with histological and clinical supporting data (advanced-mri-breast-lesions)(version 2)[data set]. the cancer imaging archive (2024)

work page 2024
[5]

Scientific data 12(1), 453 (2025)

Garrucho, L., Kushibar, K., Reidel, C.A., Joshi, S., Osuala, R., Tsirikoglou, A., Bobowicz, M., Del Riego, J., Catanese, A., Gwo \'z dziewicz, K., et al.: A large-scale multicenter breast cancer dce-mri benchmark dataset with expert segmentations. Scientific data 12(1), 453 (2025)

work page 2025
[6]

In: International Workshop on Machine Learning in Medical Imaging

Guo, S., Zhang, J., Gu, D., Gao, F., Zhan, Y., Xue, Z., Shen, D.: Contrastive learning-based breast tumor segmentation in dce-mri. In: International Workshop on Machine Learning in Medical Imaging. pp. 157--165. Springer (2023)

work page 2023
[7]

In: International Conference on Medical Image Computing and Computer-Assisted Intervention

He, Y., Nath, V., Yang, D., Tang, Y., Myronenko, A., Xu, D.: Swinunetr-v2: Stronger swin transformers with stagewise convolutions for 3d medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 416--426. Springer (2023)

work page 2023
[8]

Academic radiology 32(3), 1218--1225 (2025)

Hirsch, L., Huang, Y., Makse, H.A., Martinez, D.F., Hughes, M., Eskreis-Winkler, S., Pinker, K., Morris, E.A., Parra, L.C., Sutton, E.J.: Early detection of breast cancer in mri using ai. Academic radiology 32(3), 1218--1225 (2025)

work page 2025

Show all 22 references

[9]

Nature methods 18(2), 203--211 (2021)

Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods 18(2), 203--211 (2021)

2021
[10]

Scientific Reports 14(1), 3839 (2024)

Kebede, S.R., Waldamichael, F.G., Debelee, T.G., Aleme, M., Bedane, W., Mezgebu, B., Merga, Z.C.: Dual view deep learning for enhanced breast cancer screening using mammography. Scientific Reports 14(1), 3839 (2024)

2024
[11]

In: Proceedings of the IEEE/CVF international conference on computer vision

Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., et al.: Segment anything. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 4015--4026 (2023)

2023
[12]

In: International Conference on Medical Image Computing and Computer-Assisted Intervention

Konz, N., Chen, Y., Dong, H., Mazurowski, M.A.: Anatomically-controllable medical image generation with segmentation-guided diffusion models. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 88--98. Springer (2024)

2024
[13]

Journal of the American college of radiology 7(1), 18--27 (2010)

Lee, C.H., Dershaw, D.D., Kopans, D., Evans, P., Monsees, B., Monticciolo, D., Brenner, R.J., Bassett, L., Berg, W., Feig, S., et al.: Breast cancer screening with imaging: recommendations from the society of breast imaging and the acr on the use of mammography, breast mri, br...

2010
[14]

Nature methods 21(2), 195--212 (2024)

Maier-Hein, L., Reinke, A., Godau, P., Tizabi, M.D., Buettner, F., Christodoulou, E., Glocker, B., Isensee, F., Kleesiek, J., Kozubek, M., et al.: Metrics reloaded: recommendations for image analysis validation. Nature methods 21(2), 195--212 (2024)

2024
[15]

Diploma thesis, Universität Wien (2023), https://doi.org/10.25365/thesis.73974

Perschy, L.: Breast cancer prediction in high-risk patients using deep learning on MR imaging. Diploma thesis, Universität Wien (2023), https://doi.org/10.25365/thesis.73974

2023 doi
[16]

In: International Conference on Medical Image Computing and Computer-Assisted Intervention

Rokuss, M.R., Kirchhoff, Y., Roy, S., Kovacs, B., Ulrich, C., Wald, T., Zenk, M., Denner, S., Isensee, F., Vollmuth, P., et al.: Longitudinal segmentation of ms lesions via temporal difference weighting. In: International Conference on Medical Image Computing and Computer-Assi...

2024
[17]

British journal of cancer 119(4), 508--516 (2018)

Saha, A., Harowicz, M.R., Grimm, L.J., Kim, C.E., Ghate, S.V., Walsh, R., Mazurowski, M.A.: A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 dce-mri features. British journal of cancer 119(4), 508--516 (2018)

2018
[18]

Science translational medicine 14(664), eabo4802 (2022)

Witowski, J., Heacock, L., Reig, B., Kang, S.K., Lewin, A., Pysarenko, K., Patel, S., Samreen, N., Rudnicki, W., uczy \'n ska, E., et al.: Improving breast cancer diagnostics with deep learning for mri. Science translational medicine 14(664), eabo4802 (2022)

2022
[19]

Fact sheet, WHO News‑room (Mar 2024), https://www.who.int/news-room/fact-sheets/detail/breast-cancer, accessed 2025‑06‑12

World Health Organization : Breast cancer. Fact sheet, WHO News‑room (Mar 2024), https://www.who.int/news-room/fact-sheets/detail/breast-cancer, accessed 2025‑06‑12

2024
[20]

In: Medical Imaging 2018: Computer-Aided Diagnosis

Zhang, J., Saha, A., Zhu, Z., Mazurowski, M.A.: Breast tumor segmentation in dce-mri using fully convolutional networks with an application in radiogenomics. In: Medical Imaging 2018: Computer-Aided Diagnosis. vol. 10575, pp. 192--196. SPIE (2018)

2018
[21]

arXiv preprint arXiv:2405.12971 (2024)

Zhao, T., Gu, Y., Yang, J., Usuyama, N., Lee, H.H., Naumann, T., Gao, J., Crabtree, A., Abel, J., Moung-Wen, C., et al.: Biomedparse: a biomedical foundation model for image parsing of everything everywhere all at once. arXiv preprint arXiv:2405.12971 (2024)

2024 arXiv
[22]

Computers in Biology and Medicine 164, 107255 (2023)

Zhao, X., Liao, Y., Xie, J., He, X., Zhang, S., Wang, G., Fang, J., Lu, H., Yu, J.: Breastdm: A dce-mri dataset for breast tumor image segmentation and classification. Computers in Biology and Medicine 164, 107255 (2023)

2023

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

Website, American Cancer Society (2025), https://www.cancer.org/cancer/types/breast-cancer.html, accessed 2025‑06‑12

American Cancer Society : Breast cancer. Website, American Cancer Society (2025), https://www.cancer.org/cancer/types/breast-cancer.html, accessed 2025‑06‑12

work page 2025

[2] [2]

European Radiology Experimental 7(1), 32 (2023)

Burger, B., Bernathova, M., Seeb \"o ck, P., Singer, C.F., Helbich, T.H., Langs, G.: Deep learning for predicting future lesion emergence in high-risk breast mri screening: a feasibility study. European Radiology Experimental 7(1), 32 (2023)

work page 2023

[3] [3]

In: 2021 IEEE symposium on computers and communications (ISCC)

Carvalho, E.D., Silva, R.R.V., Mathew, M.J., Araujo, F.H.D., De Carvalho Filho, A.O.: Tumor segmentation in breast dce-mri slice using deep learning methods. In: 2021 IEEE symposium on computers and communications (ISCC). pp. 1--6. IEEE (2021)

work page 2021

[4] [4]

the cancer imaging archive (2024)

Daniels, D., Last, D., Cohen, K., Mardor, Y., Sklair-Levy, M.: Standard and delayed contrast-enhanced mri of malignant and benign breast lesions with histological and clinical supporting data (advanced-mri-breast-lesions)(version 2)[data set]. the cancer imaging archive (2024)

work page 2024

[5] [5]

Scientific data 12(1), 453 (2025)

Garrucho, L., Kushibar, K., Reidel, C.A., Joshi, S., Osuala, R., Tsirikoglou, A., Bobowicz, M., Del Riego, J., Catanese, A., Gwo \'z dziewicz, K., et al.: A large-scale multicenter breast cancer dce-mri benchmark dataset with expert segmentations. Scientific data 12(1), 453 (2025)

work page 2025

[6] [6]

In: International Workshop on Machine Learning in Medical Imaging

Guo, S., Zhang, J., Gu, D., Gao, F., Zhan, Y., Xue, Z., Shen, D.: Contrastive learning-based breast tumor segmentation in dce-mri. In: International Workshop on Machine Learning in Medical Imaging. pp. 157--165. Springer (2023)

work page 2023

[7] [7]

In: International Conference on Medical Image Computing and Computer-Assisted Intervention

He, Y., Nath, V., Yang, D., Tang, Y., Myronenko, A., Xu, D.: Swinunetr-v2: Stronger swin transformers with stagewise convolutions for 3d medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 416--426. Springer (2023)

work page 2023

[8] [8]

Academic radiology 32(3), 1218--1225 (2025)

Hirsch, L., Huang, Y., Makse, H.A., Martinez, D.F., Hughes, M., Eskreis-Winkler, S., Pinker, K., Morris, E.A., Parra, L.C., Sutton, E.J.: Early detection of breast cancer in mri using ai. Academic radiology 32(3), 1218--1225 (2025)

work page 2025