5 papers · 1 filter
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…
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…
DiffExplainer: Unveiling Black Box Models Via Counterfactual Generation
Yingying Fang, Shuang Wu, Zihao Jin +4
In the field of medical imaging, particularly in tasks related to early disease detection and prognosis, understanding the reasoning behind AI model predictions is imperative for a…
Decoding Decision Reasoning: A Counterfactual-Powered Model for Knowledge Discovery
Yingying Fang, Zihao Jin, Xiaodan Xing +2
In medical imaging, particularly in early disease detection and prognosis tasks, discerning the rationale behind an AI model's predictions is crucial for evaluating the reliability…
Make it more specific: A novel uncertainty based airway segmentation application on 3D U-Net and its variants
Shiyi Wang, Yang Nan, Felder Federico N +3
Each medical segmentation task should be considered with a specific AI algorithm based on its scenario so that the most accurate prediction model can be obtained. The most popular…