← Back to all news
arXiv Computer Vision By Qiming He, Jing Li, Zihan Wang, Tian Guan, Yifei Ma, Zimo Zhao, Yanxia Wang, Hongjing Chen, Yexing Zhang, Yizhi Wang, Xinrui Chen, Xitong Ling, Minxi Ouyang, Yujie Xie, Lianghui Zhu, Yiqing Liu, Qingxia Hou, Shuyan Zhao, Xiaoqin Wang, Lili Ma, Qiang Huang, Zhiyuan Shen, Junru Cheng, Siqi Zeng, Chao He, Yonghong He, Zhe Wang

Knowledge-structured and evidence-aware semantic self-distillation for glomerular pathology foundation model

Read the original on arXiv Computer Vision →

The paper introduces GloSAGE, a semantic self‑distillation framework that leverages lesion profiles from a pretrained visual teacher to generate structured pathology descriptions for glomerular images. By selecting reliable image‑text anchors and synthesizing knowledge‑structured text, GloSAGE trains a frozen visual encoder with an intermediate semantic transfer layer, enabling zero‑shot lesion recognition, image‑text retrieval, and improved downstream tasks such as disease classification and renal function estimation. Experiments on the GloMillion dataset and seven cohorts demonstrate that GloSAGE outperforms its source visual model in most lesion‑recognition tasks and enhances grading accuracy while maintaining competitive performance on case‑level analyses.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv AI
Sep 2

Benchmarking Vision-Language Models for Automated Pathology Diagnosis and Report Generation

arXiv:2609.00866v1 Announce Type: cross Abstract: The rapid advancement of vision-language models (VLMs) has accelerated progress in computational pathology; however, whole-slide image (WSI)-based pa...

By Yumi Lee, Harim Oh, Hyoryung Kim, Minji Kim, Eunsu Kim, Hyeseong Lee, Junya Fukuoka, Andrey Bychkov, Jijgee Munkhdelger, Rajiv Kumar Kaushal, Ayushi Sahay, Rajni Yadav, Bharathi Prabakaran, Sulen Sarioglu, Serdar Balc{\i}, Ilknur Turkmen, Yuri Tolkach, Christian Harder, Julian Westerdorf, Reinhard Buettner, Audun Ljone Henriksen, Sepp De Raedt, Byung Hyun Lee, Sungjin Lim, Joohoon Lee, Gwanghyun Kim, Se Young Chun, Suryakant Singh, Saarthak Kapse, Prateek Prasanna, Kyung A Kim, Yousun Kang, Sehwan Yoo, Sungman Hong, Shubham Innani, Michael Feldman, Spyridon Bakas, Ujjwal Baid, Prasad Dutande, Suhas Gajare, Bhakti Baheti, Serkan S\"okmen, Ece Tu\u{g}ba Cebeci, Ahmet Hal{\i}c{\i}, Musa Balc{\i}, Kardelen Pe\c{c}enek, Srividhya Sainath, Kyongseok Jang, Messi H. J. Lee, Noorul Wahab, Bodong Du, Jiaming Zhang, Qixiang Zhang, Jang-Hwan Choi, Sangjeong Ahn
llmsragmultimodalbenchmarkssafety
More like this →
arXiv Computer Vision
Oct 9

PathLang: A Language-Centered Benchmark for Vision-Language Models in Computational Pathology

PathLang is a new, language‑centered benchmark for vision‑language models in computational pathology. It keeps the underlying pathology slides, ground‑truth labels, and image‑text evaluation direction constant while systematically varying the diagnostic language used in prompts, reflecting how pathologists actually rephrase diagnoses. The benchmark covers four task families—zero‑shot classification with image‑text alignment, cross‑modal retrieval, paraphrase robustness, and open‑vocabulary diagnosis retrieval—and evaluates nine VLMs across five public datasets spanning four organs.

By Fanqi Cheng, Kuo Gong, Shangke Liu, Beidi Zhao, Junchao Zhu, Zheyu Zhu, Leiyue Zhao, Fengbei Liu, John Cannon, Gang Wang, Zu-hua Gao, Kenji Ikemura, Yihe Yang, Yaohong Wang, Yuankai Huo, Xiaoxiao Li, Mert R. Sabuncu, Ruining Deng
llmsragmultimodalbenchmarkssafety
More like this →
arXiv Machine Learning
Sep 24

FFM-CP: Cross-Backbone Fusion of Vision-Language Foundation Models for Few-Shot Computational Pathology

The paper introduces FFM-CP, a framework that fuses multiple pathology vision‑language foundation models for few‑shot learning. It aligns heterogeneous representations with an Orthogonal Procrustes transformation, then uses a unified graph to refine support‑image features and class prototypes across backbones. Experiments on six histopathology datasets show that FFM‑CP outperforms the best single adapted model in 50 of 54 few‑shot comparisons.

By Anh-Tien Nguyen, Trung DQ. Dang, Nghiem Tuong Diep, Bui Ngoc Han Nguyen, Tan-Ha Mai, Miriam Cindy Maurer, Phuong Hoa Nguyen, Thi Thuy Uyen Nguyen, Youngjun Park, Daniel Sonntag, Duy Minh Ho Nguyen, Anne-Christin Hauschild
llmsmultimodalsafety
More like this →