most citedEnsemble learning of pathology foundation models for precision oncology

2 citations · 3 across the 4 of their papers we have counts for

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cs.CV2026

Towards Scalable Context-Aware Single-Cell Spatial Transcriptomics Prediction from Histology Images

Zijun Gao, Chunbin Gu, Jinxi Xiang +2

Predicting gene expression from H&E-stained histology images offers a scalable alternative to costly spatial transcriptomics, yet most existing methods operate at the spot level, w…

cs.CV2026

A Generative Foundation Model for Multimodal Histopathology

Jinxi Xiang, Mingjie Li, Siyu Hou +9

Accurate diagnosis and treatment of complex diseases require integrating histological, molecular, and clinical data, yet in practice these modalities are often incomplete owing to…

cs.CV2025

nnMIL: A generalizable multiple instance learning framework for computational pathology

Xiangde Luo, Jinxi Xiang, Yuanfeng Ji +1

Computational pathology holds substantial promise for improving diagnosis and guiding treatment decisions. Recent pathology foundation models enable the extraction of rich patch-le…

cs.CV2025

A Generative Foundation Model for Chest Radiography

Yuanfeng Ji, Dan Lin, Xiyue Wang +12

The scarcity of well-annotated diverse medical images is a major hurdle for developing reliable AI models in healthcare. Substantial technical advances have been made in generative…

cs.CV2025★ 2 cited

Ensemble learning of pathology foundation models for precision oncology

Xiangde Luo, Xiyue Wang, Feyisope Eweje +24

Histopathology is essential for cancer diagnosis and treatment selection, and pathology foundation models learn visual representations from whole-slide images (WSIs). However, exis…