1 citations · 3 across the 7 of their papers we have counts for
4 papers · 1 filter
Learning to reason about rare diseases through retrieval-augmented agents
Ha Young Kim, Jun Li, Ana Beatriz Solana +4
Rare diseases represent the long tail of medical imaging, where AI models often fail due to the scarcity of representative training data. In clinical workflows, radiologists freque…
Knowledge to Sight: Reasoning over Visual Attributes via Knowledge Decomposition for Abnormality Grounding
Jun Li, Che Liu, Wenjia Bai +4
In this work, we address the problem of grounding abnormalities in medical images, where the goal is to localize clinical findings based on textual descriptions. While generalist V…
NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRI
Cosmin I. Bercea, Jun Li, Philipp Raffler +12
In many real-world applications, deployed models encounter inputs that differ from the data seen during training. Out-of-distribution detection identifies whether an input stems fr…
Enhancing Abnormality Grounding for Vision Language Models with Knowledge Descriptions
Jun Li, Che Liu, Wenjia Bai +3
Visual Language Models (VLMs) have demonstrated impressive capabilities in visual grounding tasks. However, their effectiveness in the medical domain, particularly for abnormality…