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20202026
most citedA Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model

9 citations · 26 across the 26 of their papers we have counts for

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6 papers · 1 filter

eess.IV2026

A Clinically Validated Foundation Model for Comprehensive Lung Pathology Interpretation

Zhengrui Guo, Zhengyu Zhang, Jiabo Ma +23

Pathological assessment guides lung cancer diagnosis, treatment selection, and prognostic evaluation, yet current CPath approaches rely on task-specific models for isolated objecti…

eess.IV2025

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…

eess.IV2025

FreeTumor: Large-Scale Generative Tumor Synthesis in Computed Tomography Images for Improving Tumor Recognition

Linshan Wu, Jiaxin Zhuang, Yanning Zhou +12

Tumor is a leading cause of death worldwide, with an estimated 10 million deaths attributed to tumor-related diseases every year. AI-driven tumor recognition unlocks new possibilit…

eess.IV2024★ 5 cited

Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Jiabo Ma, Zhengrui Guo, Fengtao Zhou +20

Foundation models pretrained on large-scale datasets are revolutionizing the field of computational pathology (CPath). The generalization ability of foundation models is crucial fo…

eess.IV2023

LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset

Yiping Jiao, Jeroen van der Laak, Shadi Albarqouni +18

We introduce LYSTO, the Lymphocyte Assessment Hackathon, which was held in conjunction with the MICCAI 2019 Conference in Shenzen (China). The competition required participants to…

eess.IV2020★ 1 cited

Reconstruct high-resolution multi-focal plane images from a single 2D wide field image

Jiabo Ma, Sibo Liu, Shenghua Cheng +3

High-resolution 3D medical images are important for analysis and diagnosis, but axial scanning to acquire them is very time-consuming. In this paper, we propose a fast end-to-end m…