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Therefore, effective chemotherapy response prediction is crucial. While whole slide image provides high-resolution insights into tumour environments, existing weakly supervised learning frameworks struggle to effectively integrate molecular data, such as gene expression, limiting their predictive power in complex chemotherapy response and small-sample scenarios. We present a bimodal chemotherapy response multi-instance learning framework, BiChemoCLAM, a novel multimodal deep learning framework that combines attention-driven multiple instance learning with multimodal compact bilinear pooling for interpretable and data-efficient chemotherapy response prediction. It achieves an Area Under Curve (AUC) of 80.91%, 71.68%, and 75.80% on ovarian serous cystadenocarcinoma, colorectal adenocarcinoma, and bladder urothelial carcinoma cancer datasets, respectively. The experimental results show that BiChemoCLAM is an effective model for predicting response to chemotherapy.<\/jats:p>","DOI":"10.1093\/bib\/bbaf728","type":"journal-article","created":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T13:04:06Z","timestamp":1766581446000},"source":"Crossref","is-referenced-by-count":2,"title":["BiChemoCLAM: a weakly supervised multimodal framework for chemotherapy response prediction"],"prefix":"10.1093","volume":"27","author":[{"given":"Jinglong","family":"Gui","sequence":"first","affiliation":[{"name":"School of Computer Software , College of Intelligence and Computing, Tianjin University, Ya Guan Road No. 135, Haihe Education Park, Jinnan District, Tianjin, 300354,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changming","family":"Sun","sequence":"additional","affiliation":[{"name":"CSIRO Data61 , PO Box 76, Epping, NSW 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361005,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Zhao","sequence":"additional","affiliation":[{"name":"Chest Hospital , Tianjin University, No. 261, Taierzhuang South Road, Jinnan District, Tianjin, 300222,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Tianjin Medical University Cancer Institute and Hospital , The First Department of Breast Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, West Huan-Hu Rd, Ti Yuan Bei, Hexi District, Tianjin, 300060,","place":["China"]},{"name":"Tianjin\u2019s Clinical Research Center for Cancer , National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, West Huan-Hu Rd, Ti Yuan Bei, Hexi District, Tianjin, 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