Multimodal Stepwise Clinically-Guided Attention Learning for Pathological Complete Response Prediction in Breast Cancer

Alice Natalina Caragliano; Carlo Sansone; Michela Gravina; Paolo Soda; Valerio Guarrasi

arxiv: 2605.07561 · v1 · submitted 2026-05-08 · 💻 cs.CV

Multimodal Stepwise Clinically-Guided Attention Learning for Pathological Complete Response Prediction in Breast Cancer

Alice Natalina Caragliano , Valerio Guarrasi , Michela Gravina , Carlo Sansone , Paolo Soda This is my paper

Pith reviewed 2026-05-11 01:58 UTC · model grok-4.3

classification 💻 cs.CV

keywords pathological complete responsebreast cancerMRIattention learningmultimodal integrationneoadjuvant therapyclinical guidanceprediction model

0 comments

The pith

A stepwise training process guides MRI models to focus on tumor regions before adding clinical data, improving detection of breast cancer treatment responders.

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

The paper introduces a multimodal framework that trains in stages to predict pathological complete response from breast MRI scans. It starts by learning broad imaging patterns, then applies attention constraints to tumor areas drawn from clinical knowledge, and finally incorporates patient variables to refine outputs. This structure addresses severe class imbalance by steering the model toward medically relevant features rather than noise. The authors show that such guidance yields attention maps aligned with anatomy and better cross-institution performance than single-stage baselines. The core idea is that embedding physician-style reasoning into the training sequence makes predictions more reliable despite limited responder cases.

Core claim

The multimodal stepwise clinically-guided attention learning framework follows a three-stage process: first learning global discriminative imaging patterns from MRI, then introducing attention mechanisms constrained to tumor regions, and finally integrating clinical variables, which together improve identification of pathological complete response responders and generate anatomically coherent attention maps that aid interpretation.

What carries the argument

The stepwise training strategy that progressively adds global imaging patterns, tumor-constrained attention, and clinical data integration to direct the network toward task-relevant features.

If this is right

Higher sensitivity in identifying pathological complete response patients than non-guided single-stage models while keeping specificity competitive.
Attention maps that remain consistent with tumor anatomy, enabling visual checks on model reasoning.
Reduced dependence on institution-specific imaging patterns through anatomical grounding, supporting broader deployment.
Better handling of class imbalance by prioritizing clinically relevant features over majority-class patterns.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same staged guidance could be tested on other imaging-based oncology tasks where tumor localization is known but data is scarce.
If the order of stages proves critical, it would imply that sequential clinical reasoning injection matters more than simultaneous multimodal fusion.
Extending the framework to include dynamic contrast-enhanced sequences or genomic markers might further refine responder stratification without retraining from scratch.

Load-bearing premise

Constraining attention to anatomically consistent tumor regions will cause the model to ignore dataset-specific artifacts and thereby generalize better across institutions.

What would settle it

External validation on new heterogeneous breast MRI cohorts from different institutions showing no gain in sensitivity or attention maps that fail to align with tumor anatomy would falsify the benefit of the guided stepwise approach.

Figures

Figures reproduced from arXiv: 2605.07561 by Alice Natalina Caragliano, Carlo Sansone, Michela Gravina, Paolo Soda, Valerio Guarrasi.

**Figure 1.** Figure 1: Overview of the proposed framework, structured into three progressive steps integrating attention mechanisms and clinically grounded guidance. 2.1 Problem Formulation Let xi ∈ R C×D×H×W denote a multi-phase 3D breast DCE-MRI input, where C corresponds to post-contrast phases, each with depth D, height H, and width W. The corresponding label vector y = {0, 1} denotes the binary pCR outcome, with y = 1 indic… view at source ↗

**Figure 2.** Figure 2: Left: Quantitative performance across training steps under the external evaluation scenarios, with colors indicating the test cohort and marker shapes denoting modality configuration. Right: Attention maps across steps for representative patients from each cohort; bounding boxes highlight lesion regions on DCE-MRI. In the smaller External-NACT cohort, overall performance is lower, yet the stepwise framewo… view at source ↗

read the original abstract

Pathological complete response (pCR) is a key prognostic factor in breast cancer patients undergoing neoadjuvant therapy, strongly associated with long-term survival and treatment personalization. However, accurate pre-treatment pCR prediction remains challenging due to severe class imbalance and limited generalizability across diverse clinical settings. In this work, we propose a multimodal stepwise clinically-guided attention learning framework for pCR prediction from breast magnetic resonance imaging (MRI), designed to address these limitations through medically grounded spatial guidance and multimodal integration. The approach follows a stepwise training strategy inspired by physician reasoning: the model first learns global discriminative imaging patterns, then attention mechanisms are introduced to constrain the network toward tumor regions, and finally clinical variables are integrated to refine decision-making. This guidance strategy encourages prioritization of task-relevant features, improving identification of responders despite their limited representation in the dataset. Moreover, grounding attention in anatomically consistent tumor regions reduces reliance on dataset-specific patterns, thereby enhancing cross-institutional generalization. The framework is evaluated through external validation across heterogeneous MRI cohorts. Compared to non-guided single-stage baselines, the proposed approach improves sensitivity while maintaining competitive specificity, and produces anatomically coherent attention maps that support interpretation of the model's predictions. These findings highlight the potential of clinically-guided multimodal attention learning for robust and generalizable pCR prediction in breast cancer.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The stepwise multimodal attention model for pCR prediction is motivated well but the abstract leaves the source of gains unclear without ablations.

read the letter

The main point is that this paper outlines a three-stage training process for a multimodal attention network that predicts pathological complete response from breast MRI: global patterns first, then tumor-region attention, then clinical variables added last. The approach is new in tying the stages explicitly to clinical reasoning and in claiming external validation on heterogeneous cohorts. It does a reasonable job motivating the design for class imbalance and generalization, and the mention of anatomically coherent attention maps is a plus for interpretability in a medical context. External validation is the right direction for this kind of work. The soft spots are real and central. The description supplies no numbers, no baseline scores, no statistical tests, and no ablation results. Because the only comparisons are to single-stage non-guided models, it is impossible to separate the effect of the stepwise schedule from the specific clinical guidance or tumor-region constraint. The stress-test concern lands: the claim that grounding attention in tumor regions reduces dataset-specific patterns is not yet secured by the reported controls. This is not a minor omission. The paper is aimed at researchers in medical image analysis and oncology AI who work on attention mechanisms or multimodal fusion for imbalanced prediction tasks. Someone building similar systems might borrow the staged training idea, but the current evidence is too thin for broad adoption. It deserves peer review. The clinical problem is important, the framework is coherently described, and referees can push for the missing metrics and ablations that would make the contribution clearer.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes a multimodal stepwise clinically-guided attention learning framework for predicting pathological complete response (pCR) from breast MRI. The method follows a three-stage training process inspired by physician reasoning: first learning global discriminative imaging patterns, then constraining attention mechanisms to tumor regions, and finally integrating clinical variables. It is evaluated via external validation on heterogeneous MRI cohorts and claims improved sensitivity (while maintaining competitive specificity), enhanced cross-institutional generalization, and anatomically coherent attention maps relative to non-guided single-stage baselines.

Significance. If the central claims hold after appropriate controls, the work could advance interpretable multimodal AI for pCR prediction, a clinically important task for personalizing neoadjuvant therapy in breast cancer. The emphasis on anatomically grounded attention and stepwise integration offers a promising direction for addressing class imbalance and generalization challenges common in medical imaging.

major comments (2)

[Abstract] Abstract: The assertion that 'grounding attention in anatomically consistent tumor regions reduces reliance on dataset-specific patterns, thereby enhancing cross-institutional generalization' is not secured by the reported experiments. The design compares the full multi-stage guided model only against single-stage non-guided baselines, without ablations that isolate the contribution of the tumor-region constraint or clinical integration from the stepwise optimization schedule itself (e.g., curriculum-like regularization effects).
[Abstract] Abstract and Results: No quantitative metrics (sensitivity, specificity, AUC, p-values, or cohort sizes), baseline details, or statistical analysis are supplied to support the claimed improvements in sensitivity and generalization, leaving the central empirical claims without verifiable support from presented evidence.

minor comments (1)

[Abstract] The abstract would benefit from a concise statement of the specific external validation cohorts and their heterogeneity to strengthen the generalizability narrative.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point by point below, providing clarifications and indicating revisions where the experimental design or presentation can be strengthened without misrepresenting our results.

read point-by-point responses

Referee: [Abstract] Abstract: The assertion that 'grounding attention in anatomically consistent tumor regions reduces reliance on dataset-specific patterns, thereby enhancing cross-institutional generalization' is not secured by the reported experiments. The design compares the full multi-stage guided model only against single-stage non-guided baselines, without ablations that isolate the contribution of the tumor-region constraint or clinical integration from the stepwise optimization schedule itself (e.g., curriculum-like regularization effects).

Authors: We acknowledge that the current experiments compare the complete multi-stage model against single-stage non-guided baselines and do not include explicit ablations that decouple the tumor-region constraint and clinical integration from the stepwise schedule. To address this, we will add ablation studies in the revised manuscript that progressively enable the guidance components while holding the training schedule fixed. These results will be reported alongside the existing external validation to better substantiate the contribution of anatomically grounded attention to generalization. revision: yes
Referee: [Abstract] Abstract and Results: No quantitative metrics (sensitivity, specificity, AUC, p-values, or cohort sizes), baseline details, or statistical analysis are supplied to support the claimed improvements in sensitivity and generalization, leaving the central empirical claims without verifiable support from presented evidence.

Authors: We agree that the abstract as written lacks the specific quantitative metrics needed to support the claims. The results section contains the full set of metrics (sensitivity, specificity, AUC, p-values, cohort sizes, baseline details, and statistical tests) from the external validation cohorts. We will revise the abstract to incorporate the key numerical results and will add a brief summary of the statistical analysis to ensure the central claims are directly supported by evidence in the abstract. revision: yes

Circularity Check

0 steps flagged

No circularity: novel stepwise training strategy with external validation

full rationale

The paper describes a multimodal stepwise clinically-guided attention learning framework consisting of three sequential training phases (global pattern learning, tumor-region attention constraint, clinical variable integration) evaluated via external validation on heterogeneous MRI cohorts. No mathematical equations, fitted parameters, or predictions are presented that reduce by construction to inputs defined within the paper. Claims of improved sensitivity and generalization rest on empirical comparisons to non-guided single-stage baselines rather than definitional loops or self-citation chains. The derivation is self-contained as an architectural proposal without any of the enumerated circular patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The abstract introduces a new training strategy but provides no details on model hyperparameters or specific parameters; relies on standard assumptions in medical imaging AI.

axioms (2)

domain assumption Stepwise training inspired by physician reasoning improves feature prioritization and generalization in neural networks for medical prediction tasks
Invoked in the description of the framework design and its motivation.
domain assumption MRI features combined with clinical variables can predict pathological complete response
Core premise underlying the multimodal integration and prediction goal.

pith-pipeline@v0.9.0 · 5547 in / 1377 out tokens · 47173 ms · 2026-05-11T01:58:00.393465+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

The approach follows a stepwise training strategy inspired by physician reasoning: the model first learns global discriminative imaging patterns, then attention mechanisms are introduced to constrain the network toward tumor regions, and finally clinical variables are integrated
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

grounding attention in anatomically consistent tumor regions reduces reliance on dataset-specific patterns

What do these tags mean?

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supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages

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In: Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care

Awwad, H., Vilanova, J.C., Martí, R.: Can We Teach AI to Understand Breast Tu- mour Behaviour? Our MAMA-MIA Challenge Journey. In: Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care. pp. 248–257. Springer (2025)

work page 2025
[2]

Frontiers in oncology10, 93 (2020)

Cheng, Q., Huang, J., Liang, J., Ma, M., Ye, K., Shi, C., Luo, L.: The diagnostic performance of DCE-MRI in evaluating the pathological response to neoadjuvant chemotherapy in breast cancer: a meta-analysis. Frontiers in oncology10, 93 (2020)

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Cortazar, P., Zhang, L., Untch, M., Mehta, K., Costantino, J.P., Wolmark, N., Bonnefoi, H., Cameron, D., Gianni, L., Valagussa, P., et al.: Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. The Lancet384(9938), 164–172 (2014)

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[4]

Plos one18(1), e0280148 (2023)

Dammu, H., Ren, T., Duong, T.Q.: Deep learning prediction of pathological com- plete response, residual cancer burden, and progression-free survival in breast can- cer patients. Plos one18(1), e0280148 (2023)

work page 2023
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arXiv preprint arXiv:2506.12190 (2025)

Fridman, N., Solway, B., Fridman, T., Barnea, I., Goldstein, A.: BreastDCEDL: A Comprehensive Breast Cancer DCE-MRI Dataset and Transformer Implementa- tion for Treatment Response Prediction. arXiv preprint arXiv:2506.12190 (2025)

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Garrucho, L., Kushibar, K., Reidel, C.A., Joshi, S., Osuala, R., Tsirikoglou, A., Bobowicz, M., Riego, J.d., Catanese, A., Gwoździewicz, K., Cosaka, M.L., Abo- Elhoda, P.M., Tantawy, S.W., Sakrana, S.S., Shawky-Abdelfatah, N.O., Salem, A.M.A., Kozana, A., Divjak, E., Ivanac, G., Nikiforaki, K., Klontzas, M.E., García- Dosdá, R., Gulsun-Akpinar, M., Lafcı,...

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Cochrane database of sys- tematic reviews (2) (2007)

van der Hage, J.H., van de Velde, C.J., Mieog, S.J., Charehbili, A.: Preoperative chemotherapy for women with operable breast cancer. Cochrane database of sys- tematic reviews (2) (2007)

work page 2007
[8]

Journal of Magnetic Resonance Imaging52(5), 1360–1373 (2020)

Kang, S.R., Kim, H.W., Kim, H.S.: Evaluating the relationship between dynamic contrast-enhanced MRI (DCE-MRI) parameters and pathological characteristics in breast cancer. Journal of Magnetic Resonance Imaging52(5), 1360–1373 (2020)

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Caragliano et al

Li, W., Newitt, D.C., Gibbs, J., Wilmes, L.J., Jones, E.F., Arasu, V.A., Strand, F., Onishi, N., Nguyen, A.A.T., Kornak, J., Joe, B.N., Price, E.R., Ojeda-Fournier, H., Eghtedari, M., Zamora, K.W., Woodard, S.A., Umphrey, H., Bernreuter, W., Nelson, M., Hylton, N.M.: I-SPY 2 Breast Dynamic Contrast Enhanced MRI Trial (ISPY2)(Version1).TheCancerImagingArch...

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[10]

In: Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Chal- lenges in Breast Care

Musah, T.: Large Kernel MedNeXt for Breast Tumor Segmentation and Self- normalizing Network for pCR Classification in Magnetic Resonance Images. In: Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Chal- lenges in Breast Care. pp. 72–80. Springer (2025)

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[11]

The Cancer Imaging Archive (2016)

Newitt, D., Hylton, N.: Single site breast DCE-MRI data and segmentations from patients undergoing neoadjuvant chemotherapy (Version 3). The Cancer Imaging Archive (2016). https://doi.org/10.7937/K9/TCIA.2016.QHsyhJKy

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The Cancer Imaging Archive (2016)

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and Grimm, Lars J

Saha, A., Harowicz, M.R., Grimm, L.J., Weng, J., Cain, E.H., Kim, C.E., Ghate, S.V., Walsh, R., Mazurowski, M.A.: Dynamic contrast-enhanced magnetic reso- nance images of breast cancer patients with tumor locations. The Cancer Imaging Archive (2021). https://doi.org/10.7937/TCIA.e3sv-re93

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Clinical cancer research26(12), 2838–2848 (2020)

Spring, L.M., Fell, G., Arfe, A., Sharma, C., Greenup, R., Reynolds, K.L., Smith, B.L.,Alexander,B.,Moy,B.,Isakoff,S.J.,etal.:Pathologiccompleteresponseafter neoadjuvant chemotherapy and impact on breast cancer recurrence and survival: a comprehensive meta-analysis. Clinical cancer research26(12), 2838–2848 (2020)

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In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Tang, Y., Yang, D., Li, W., Roth, H.R., Landman, B., Xu, D., Nath, V., Hatamizadeh, A.: Self-supervised pre-training of swin transformers for 3d med- ical image analysis. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 20730–20740 (2022)

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IEEE Access (2025)

Yeon, Y., Lee, H.J., Kim, J.S., An, D., Lee, J.H., Lee, S.: BR-Mix3DNet: Predicting Breast Neoadjuvant Chemotherapy Response With a Hybrid MLP-Mixer and 3D CNN Model. IEEE Access (2025)

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[1] [1]

In: Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care

Awwad, H., Vilanova, J.C., Martí, R.: Can We Teach AI to Understand Breast Tu- mour Behaviour? Our MAMA-MIA Challenge Journey. In: Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care. pp. 248–257. Springer (2025)

work page 2025

[2] [2]

Frontiers in oncology10, 93 (2020)

Cheng, Q., Huang, J., Liang, J., Ma, M., Ye, K., Shi, C., Luo, L.: The diagnostic performance of DCE-MRI in evaluating the pathological response to neoadjuvant chemotherapy in breast cancer: a meta-analysis. Frontiers in oncology10, 93 (2020)

work page 2020

[3] [3]

The Lancet384(9938), 164–172 (2014)

Cortazar, P., Zhang, L., Untch, M., Mehta, K., Costantino, J.P., Wolmark, N., Bonnefoi, H., Cameron, D., Gianni, L., Valagussa, P., et al.: Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. The Lancet384(9938), 164–172 (2014)

work page 2014

[4] [4]

Plos one18(1), e0280148 (2023)

Dammu, H., Ren, T., Duong, T.Q.: Deep learning prediction of pathological com- plete response, residual cancer burden, and progression-free survival in breast can- cer patients. Plos one18(1), e0280148 (2023)

work page 2023

[5] [5]

arXiv preprint arXiv:2506.12190 (2025)

Fridman, N., Solway, B., Fridman, T., Barnea, I., Goldstein, A.: BreastDCEDL: A Comprehensive Breast Cancer DCE-MRI Dataset and Transformer Implementa- tion for Treatment Response Prediction. arXiv preprint arXiv:2506.12190 (2025)

work page arXiv 2025

[6] [6]

Scientific Data12(1), 453 (2025)

Garrucho, L., Kushibar, K., Reidel, C.A., Joshi, S., Osuala, R., Tsirikoglou, A., Bobowicz, M., Riego, J.d., Catanese, A., Gwoździewicz, K., Cosaka, M.L., Abo- Elhoda, P.M., Tantawy, S.W., Sakrana, S.S., Shawky-Abdelfatah, N.O., Salem, A.M.A., Kozana, A., Divjak, E., Ivanac, G., Nikiforaki, K., Klontzas, M.E., García- Dosdá, R., Gulsun-Akpinar, M., Lafcı,...

work page 2025

[7] [7]

Cochrane database of sys- tematic reviews (2) (2007)

van der Hage, J.H., van de Velde, C.J., Mieog, S.J., Charehbili, A.: Preoperative chemotherapy for women with operable breast cancer. Cochrane database of sys- tematic reviews (2) (2007)

work page 2007

[8] [8]

Journal of Magnetic Resonance Imaging52(5), 1360–1373 (2020)

Kang, S.R., Kim, H.W., Kim, H.S.: Evaluating the relationship between dynamic contrast-enhanced MRI (DCE-MRI) parameters and pathological characteristics in breast cancer. Journal of Magnetic Resonance Imaging52(5), 1360–1373 (2020)

work page 2020

[9] [9]

Caragliano et al

Li, W., Newitt, D.C., Gibbs, J., Wilmes, L.J., Jones, E.F., Arasu, V.A., Strand, F., Onishi, N., Nguyen, A.A.T., Kornak, J., Joe, B.N., Price, E.R., Ojeda-Fournier, H., Eghtedari, M., Zamora, K.W., Woodard, S.A., Umphrey, H., Bernreuter, W., Nelson, M., Hylton, N.M.: I-SPY 2 Breast Dynamic Contrast Enhanced MRI Trial (ISPY2)(Version1).TheCancerImagingArch...

work page 2022

[10] [10]

In: Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Chal- lenges in Breast Care

Musah, T.: Large Kernel MedNeXt for Breast Tumor Segmentation and Self- normalizing Network for pCR Classification in Magnetic Resonance Images. In: Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Chal- lenges in Breast Care. pp. 72–80. Springer (2025)

work page 2025

[11] [11]

The Cancer Imaging Archive (2016)

Newitt, D., Hylton, N.: Single site breast DCE-MRI data and segmentations from patients undergoing neoadjuvant chemotherapy (Version 3). The Cancer Imaging Archive (2016). https://doi.org/10.7937/K9/TCIA.2016.QHsyhJKy

work page doi:10.7937/k9/tcia.2016.qhsyhjky 2016

[12] [12]

The Cancer Imaging Archive (2016)

Newitt,D.,Hylton,N.,etal.:MulticenterbreastDCE-MRIdataandsegmentations from patients in the I-SPY 1/ACRIN 6657 trials. The Cancer Imaging Archive (2016). https://doi.org/10.7937/K9/TCIA.2016.HdHpgJLK

work page doi:10.7937/k9/tcia.2016.hdhpgjlk 2016

[13] [13]

Organization, W.H.: Breast cancer, available online at: https://www.who.int/ news-room/fact-sheets/detail/breast-cancer

work page

[14] [14]

IEEE signal processing magazine34(6), 96–108 (2017)

Ramachandram, D., Taylor, G.W.: Deep multimodal learning: A survey on recent advances and trends. IEEE signal processing magazine34(6), 96–108 (2017)

work page 2017

[15] [15]

and Grimm, Lars J

Saha, A., Harowicz, M.R., Grimm, L.J., Weng, J., Cain, E.H., Kim, C.E., Ghate, S.V., Walsh, R., Mazurowski, M.A.: Dynamic contrast-enhanced magnetic reso- nance images of breast cancer patients with tumor locations. The Cancer Imaging Archive (2021). https://doi.org/10.7937/TCIA.e3sv-re93

work page doi:10.7937/tcia.e3sv-re93 2021

[16] [16]

Clinical cancer research26(12), 2838–2848 (2020)

Spring, L.M., Fell, G., Arfe, A., Sharma, C., Greenup, R., Reynolds, K.L., Smith, B.L.,Alexander,B.,Moy,B.,Isakoff,S.J.,etal.:Pathologiccompleteresponseafter neoadjuvant chemotherapy and impact on breast cancer recurrence and survival: a comprehensive meta-analysis. Clinical cancer research26(12), 2838–2848 (2020)

work page 2020

[17] [17]

In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Tang, Y., Yang, D., Li, W., Roth, H.R., Landman, B., Xu, D., Nath, V., Hatamizadeh, A.: Self-supervised pre-training of swin transformers for 3d med- ical image analysis. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 20730–20740 (2022)

work page 2022

[18] [18]

IEEE Access (2025)

Yeon, Y., Lee, H.J., Kim, J.S., An, D., Lee, J.H., Lee, S.: BR-Mix3DNet: Predicting Breast Neoadjuvant Chemotherapy Response With a Hybrid MLP-Mixer and 3D CNN Model. IEEE Access (2025)

work page 2025