9 citations · 12 across the 6 of their papers we have counts for
6 papers
PA-MIL: Phenotype-Aware Multiple Instance Learning Guided by Language Prompting and Genotype-to-Phenotype Relationships
Zekang Yang, Hong Liu, Xiangdong Wang
Deep learning has been extensively researched in the analysis of pathology whole-slide images (WSIs). However, most existing methods are limited to providing prediction interpretab…
Listening Between the Frames: Bridging Temporal Gaps in Large Audio-Language Models
Hualei Wang, Yiming Li, Shuo Ma +2
Recent Large Audio-Language Models (LALMs) exhibit impressive capabilities in understanding audio content for conversational QA tasks. However, these models struggle to accurately…
A Multimodal Object-level Contrast Learning Method for Cancer Survival Risk Prediction
Zekang Yang, Hong Liu, Xiangdong Wang
Computer-aided cancer survival risk prediction plays an important role in the timely treatment of patients. This is a challenging weakly supervised ordinal regression task associat…
Advancing Multi-grained Alignment for Contrastive Language-Audio Pre-training
Yiming Li, Zhifang Guo, Xiangdong Wang +1
Recent advances have been witnessed in audio-language joint learning, such as CLAP, that shows much success in multi-modal understanding tasks. These models usually aggregate uni-m…
SCMIL: Sparse Context-aware Multiple Instance Learning for Predicting Cancer Survival Probability Distribution in Whole Slide Images
Zekang Yang, Hong Liu, Xiangdong Wang
Cancer survival prediction is a challenging task that involves analyzing of the tumor microenvironment within Whole Slide Image (WSI). Previous methods cannot effectively capture t…
Breast Cancer Molecular Subtypes Prediction on Pathological Images with Discriminative Patch Selecting and Multi-Instance Learning
Hong Liu, Wen-Dong Xu, Zi-Hao Shang +11
Molecular subtypes of breast cancer are important references to personalized clinical treatment. For cost and labor savings, only one of the patient's paraffin blocks is usually se…