Abstract:Multimodal medical large language models have shown substantial progress in chest X-ray interpretation but continue to face challenges in spatial reasoning and anatomical understanding. Although existing grounding techniques improve overall performance, they often fail to establish a true anatomical correspondence, resulting in incorrect anatomical understanding in the medical domain. To address this gap, we introduce AnatomiX, a multitask multimodal large language model for anatomically grounded chest X-ray interpretation. Inspired by the radiological workflow, AnatomiX adopts a two stage approach: first, it identifies anatomical structures and extracts their features, and then leverages a large language model to perform diverse downstream tasks such as phrase grounding, report generation, visual question answering, and image understanding. Extensive experiments across multiple benchmarks demonstrate that AnatomiX achieves superior anatomical reasoning and delivers over 25% improvement in performance on anatomy grounding, phrase grounding, grounded diagnosis and grounded captioning tasks compared to existing approaches. Code and pretrained model are available at this https URL
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2601.03191 [cs.CV] |
| (or arXiv:2601.03191v3 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2601.03191 arXiv-issued DOI via DataCite |
From: Anees Ur Rehman Hashmi [view email]
[v1]
Tue, 6 Jan 2026 17:13:23 UTC (1,605 KB)
[v2]
Fri, 13 Mar 2026 14:10:10 UTC (7,579 KB)
[v3]
Sat, 23 May 2026 10:05:21 UTC (6,515 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。