Large Kernel MedNeXt for Breast Tumor Segmentation and Self-Normalizing Network for pCR Classification in Magnetic Resonance Images

TL;DR

This paper introduces a large-kernel MedNeXt model for improved breast tumor segmentation in MRI and a self-normalizing network for predicting pathological complete response, demonstrating enhanced performance through innovative architecture and feature extraction methods.

Contribution

It presents a novel large-kernel MedNeXt architecture with a two-stage training strategy and combines radiomics with self-normalizing networks for pCR classification in breast MRI.

Findings

Achieved a Dice score of 0.67 in segmentation

Normalized Hausdorff Distance of 0.24

Up to 75% accuracy in pCR classification in some subgroups

Abstract

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