大卷积核网络提升乳腺肿瘤分割，结合影像组学实现新辅助治疗反应预测。
Large Kernel MedNeXt for Breast Tumor Segmentation and Self-Normalizing Network for pCR Classification in Magnetic Resonance Images
- 采用大卷积核MedNeXt架构，通过UpKern算法扩展感受野至5×5×5，提升分割能力。
- 分割模型在未见数据上达Dice 0.67、NormHD 0.24；pCR分类平衡准确率最高达75%。
- 融合影像组学与自归一化网络，适合医学影像分析与精准治疗评估研究者参考。
动态对比增强磁共振成像（DCE-MRI）中精确的乳腺肿瘤分割对后续病理完全缓解（pCR）评估至关重要。本文基于大规模MAMA-MIA DCE-MRI数据集，采用大卷积核MedNeXt架构并结合两阶段训练策略，利用UpKern算法将感受野从3×3×3扩展至5×5×5，稳定迁移大核特征，提升在未见验证集上的分割性能。多模型集成实现Dice分数0.67、归一化豪斯多夫距离（NormHD）0.24。针对pCR分类，基于预测分割结果及首次增强后DCE-MRI提取的影像组学特征，训练自归一化网络（SNN），平均平衡准确率达57%，部分亚组最高达75%。研究揭示了大感受野与影像组学驱动分类的协同优势，并推动未来在高级集成与临床变量融合方面的探索。代码已开源。
原文摘要 · Abstract (English)
Accurate breast tumor segmentation in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is important for downstream tasks such as pathological complete response (pCR) assessment. In this work, we address both segmentation and pCR classification using the large-scale MAMA-MIA DCE-MRI dataset. We employ a large-kernel MedNeXt architecture with a two-stage training strategy that expands the receptive field from 3x3x3 to 5x5x5 kernels using the UpKern algorithm. This approach allows stable transfer of learned features to larger kernels, improving segmentation performance on the unseen validation set. An ensemble of large-kernel models achieved a Dice score of 0.67 and a normalized Hausdorff Distance (NormHD) of 0.24. For pCR classification, we trained a self-normalizing network (SNN) on radiomic features extracted from the predicted segmentations and first post-contrast DCE-MRI, reaching an average balanced accuracy of 57\%, and up to 75\% in some subgroups. Our findings highlight the benefits of combining larger receptive fields and radiomics-driven classification while motivating future work on advanced ensembling and the integration of clinical variables to further improve performance and generalization. Code: https://github.com/toufiqmusah/caladan-mama-mia.git
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