9 papers
Towards Fine-Grained and Verifiable Concept Bottleneck Models
Yingying Fang, Haijie Xu, Shuang Wu +2
Concept Bottleneck Models (CBMs) offer interpretable alternatives to black-box predictors by introducing human-relatable concepts before the final output. However, existing CBMs st…
MMRareBench: A Rare-Disease Multimodal and Multi-Image Medical Benchmark
Junzhi Ning, Jiashi Lin, Yingying Fang +9
Multimodal large language models (MLLMs) have advanced clinical tasks for common conditions, but their performance on rare diseases remains largely untested. In rare-disease scenar…
Seeing Through Experts Eyes A Foundational Vision Language Model Trained on Radiologists Gaze and Reasoning
Kinhei Lee, Peiyuan Jing, Zhenxuan Zhang +5
Large scale vision language models have shown promise in automating chest Xray interpretation, yet their clinical utility remains limited by a gap between model outputs and radiolo…
Learning Robust Visual Features in Computed Tomography Enables Efficient Transfer Learning for Clinical Tasks
Rubén Moreno-Aguado, Alba Magallón, Victor Moreno +2
There is substantial interest in developing artificial intelligence systems to support radiologists across tasks ranging from segmentation to report generation. Existing computed t…
Unleashing Video Language Models for Fine-grained HRCT Report Generation
Yingying Fang, Huichi Zhou, KinHei Lee +4
Generating precise diagnostic reports from High-Resolution Computed Tomography (HRCT) is critical for clinical workflow, yet it remains a formidable challenge due to the high patho…
Reason Like a Radiologist: Chain-of-Thought and Reinforcement Learning for Verifiable Report Generation
Peiyuan Jing, Kinhei Lee, Zhenxuan Zhang +7
Radiology report generation is critical for efficiency but current models lack the structured reasoning of experts, hindering clinical trust and explainability by failing to link v…