3 citations · 3 across the 7 of their papers we have counts for
8 papers
MambaMIL+: Modeling Long-Term Contextual Patterns for Gigapixel Whole Slide Image
Qian Zeng, Yihui Wang, Shu Yang +9
Whole-slide images (WSIs) are an important data modality in computational pathology, yet their gigapixel resolution and lack of fine-grained annotations challenge conventional deep…
LLM-driven Knowledge Enhancement for Multimodal Cancer Survival Prediction
Chenyu Zhao, Yingxue Xu, Fengtao Zhou +2
Current multimodal survival prediction methods typically rely on pathology images (WSIs) and genomic data, both of which are high-dimensional and redundant, making it difficult to…
GenAR: Next-Scale Autoregressive Generation for Spatial Gene Expression Prediction
Jiarui Ouyang, Yihui Wang, Yihang Gao +3
Spatial Transcriptomics (ST) offers spatially resolved gene expression but remains costly. Predicting expression directly from widely available Hematoxylin and Eosin (H&E) stained…
A Multimodal Foundation Model to Enhance Generalizability and Data Efficiency for Pan-cancer Prognosis Prediction
Huajun Zhou, Fengtao Zhou, Jiabo Ma +6
Multimodal data provides heterogeneous information for a holistic understanding of the tumor microenvironment. However, existing AI models often struggle to harness the rich inform…
A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model
Zhe Xu, Ziyi Liu, Junlin Hou +13
Multimodal large language models (MLLMs) have emerged as powerful tools for computational pathology, offering unprecedented opportunities to integrate pathological images with lang…
Genome-Anchored Foundation Model Embeddings Improve Molecular Prediction from Histology Images
Cheng Jin, Fengtao Zhou, Yunfang Yu +13
Precision oncology requires accurate molecular insights, yet obtaining these directly from genomics is costly and time-consuming for broad clinical use. Predicting complex molecula…