1 citations · 1 across the 4 of their papers we have counts for
9 papers · 1 filter
Predicting Immune Biomarkers with MultiModal Mixture-of-Expert Pathology Foundation Models Empowers Precision Oncology
Tianyu Liu, Ziqing Wang, Zhaokang Liang +12
Predicting immune biomarkers associated with the tumor immune microenvironment (TIME) is critical for advancing precision oncology, yet existing approaches are largely limited to s…
BrainAnytime: Anatomy-Aware Cross-Modal Pretraining for Brain Image Analysis with Arbitrary Modality Availability
Guangqian Yang, Tong Ding, Wenlong Hou +4
Clinical diagnostic workups typically follow a modality escalation pathway: after initial clinical evaluation, clinicians begin with routine structural imaging (e.g., MRI), selecti…
Evidence-based diagnostic reasoning with multi-agent copilot for human pathology
Luca L. Weishaupt, Chengkuan Chen, Drew F. K. Williamson +8
Pathology is experiencing rapid digital transformation driven by whole-slide imaging and artificial intelligence (AI). While deep learning-based computational pathology has achieve…
Do Multiple Instance Learning Models Transfer?
Daniel Shao, Richard J. Chen, Andrew H. Song +4
Multiple Instance Learning (MIL) is a cornerstone approach in computational pathology (CPath) for generating clinically meaningful slide-level embeddings from gigapixel tissue imag…
A Foundation Model for Spatial Proteomics
Muhammad Shaban, Yuzhou Chang, Huaying Qiu +57
Foundation models have begun to transform image analysis by acting as pretrained generalist backbones that can be adapted to many tasks even when post-training data are limited, ye…
Tree of Attributes Prompt Learning for Vision-Language Models
Tong Ding, Wanhua Li, Zhongqi Miao +1
Prompt learning has proven effective in adapting vision language models for downstream tasks. However, existing methods usually append learnable prompt tokens solely with the categ…