5 papers
PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities
Kai Yu, Shuang Zhou, Yiran Song +9
Multimodal self-supervised pretraining offers a promising route to cancer prognosis by integrating histopathology whole-slide images, gene expression, and pathology reports, yet mo…
To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering
Zaifu Zhan, Min Zeng, Shuang Zhou +6
Objective: To improve the efficiency of medical question answering (MedQA) with large language models (LLMs) by avoiding unnecessary reasoning while maintaining accuracy. Methods:…
MeCaMIL: Causality-Aware Multiple Instance Learning for Fair and Interpretable Whole Slide Image Diagnosis
Yiran Song, Yikai Zhang, Shuang Zhou +6
Multiple instance learning (MIL) has emerged as the dominant paradigm for whole slide image (WSI) analysis in computational pathology, achieving strong diagnostic performance throu…
Two-Stage Decoupling Framework for Variable-Length Glaucoma Prognosis
Yiran Song, Yikai Zhang, Silvia Orengo-Nania +5
Glaucoma is one of the leading causes of irreversible blindness worldwide. Glaucoma prognosis is essential for identifying at-risk patients and enabling timely intervention to prev…
Continually Evolved Multimodal Foundation Models for Cancer Prognosis
Jie Peng, Shuang Zhou, Longwei Yang +7
Cancer prognosis is a critical task that involves predicting patient outcomes and survival rates. To enhance prediction accuracy, previous studies have integrated diverse data moda…