bibtext: @InProceedings{ Zha_M2Fusion_MICCAI2024,
                   author = { Zhang, Song and Du, Siyao and Sun, Caixia and Li, Bao and Shao, Lizhi and Zhang, Lina and Wang, Kun and Liu, Zhenyu and Tian, Jie },
                   title = { { M2Fusion: Multi-time Multimodal Fusion for Prediction of Pathological Complete Response in Breast Cancer } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15005 },
                   month = {October},
                   pages = { pending },
                }
abstract: Accurate identification of patients who achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) is critical before surgery for guiding customized treatment regimens and assessing prognosis in breast cancer. However, current methods for predicting pCR primarily rely on single modality data or single time-point images, which fail to capture tumor changes and comprehensively represent tumor heterogeneity at both macro and micro levels. Additionally, complementary information between modalities is not fully interacted. In this paper, we present M2Fusion, pioneering the fusion of multi-time multimodal data for treatment response prediction, with two key components: the multi-time magnetic resonance imagings (MRIs) contrastive learning loss that learns representations reflecting NAC-induced tumor changes; the orthogonal multimodal fusion module that integrates orthogonal information from MRIs and whole slide images (WSIs). To evaluate the proposed M2Fusion, we collect pre-treatment MRI, post-treatment MRI, and WSIs of biopsy from patients with breast cancer at two different collaborating hospitals, each with the pCR assessed by the standard pathological procedure. Experimental results quantitatively reveal that the proposed M2Fusion improves treatment response prediction and outperforms other multimodal fusion methods and single-modality approaches. Validation on external test sets further demonstrates the generalization and validity of the model. Our code is available at https://github.com/SongZHS/M2Fusion.
title: M2Fusion: Multi-time Multimodal Fusion for Prediction of Pathological Complete Response in Breast Cancer
authors: Zhang, Song Du, Siyao Sun, Caixia Li, Bao Shao, Lizhi Zhang, Lina Wang, Kun Liu, Zhenyu Tian, Jie