Hugging Face Trending Papers

Do Pathology Vision-Language Models Truly See Pathology?

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Pathology vision-language models (VLMs) have recently progressed rapidly and are commonly evaluated by answer accuracy on pathology VQA benchmarks. However, we dig into current evaluations and identify three overlooked issues: 1) Visual evidence is not always necessary.

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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
Hugging Face Trending Papers
Jun 29

SHOVIR: A Benchmark for Evaluating Vision Shortcut Learning in Radiology Report Generation

Current evaluation protocols for Vision-Language Models (VLMs) in Radiology Report Generation (RRG) rely on report-level metrics that measure lexical overlap or aggregate clinical correctness. However, such metrics do not test whether individual diagnostic statements stem from the actual pathological evidence visible in the image.

arXiv AI
Sep 10

Semantic Context-aware mOdality fUsion Transformer (SCOUT): A Context-Aware Multimodal Transformer for Concept-Grounded Pathology Report Generation

SCOUT is a concept‑grounded multimodal transformer that generates whole‑slide pathology reports by integrating local histological patterns, whole‑slide context, and expert‑curated diagnostic concepts. It uses evolving visual representations and recursively updated slide‑ and concept‑conditioned representations, with separate attention pathways during decoding that are fused adaptively for each token. Evaluated on TCGA‑BRCA, HistAI, and REG‑2025, SCOUT outperformed existing methods, improving BLEU, METEOR, and ROUGE‑L scores and raising the Clinical Report Quality Score on REG‑2025.

By Suryakant Singh, Saarthak Kapse, Joel Saltz, Prateek Prasanna