arXiv Machine Learning By Yanmeng Dong, Han Li, Yujia Li, Jingsong Liu, Xun Ma, Yanzhu Hu, Zhengyang Xu, Zhicheng Li, Nassir Navab, Shaohua Kevin Zhou

Policy-Driven CT-Agent: Modeling Phase-Aware Diagnostic Control for Clinically Consistent CT Reasoning

Read the original on arXiv Machine Learning →

arXiv:2607. 10748v1 Announce Type: new Abstract: Computed Tomography (CT) diagnosis often relies on dynamic selection of imaging phases, such as non-contrast, arterial, or venous phases, based on preliminary findings, clinical suspicion, and diagnostic guidelines.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI
Jun 16

XMedFusion: A Knowledge-Guided Multimodal Perception and Reasoning Framework for Autonomous Medical Systems

arXiv:2606. 14766v1 Announce Type: cross Abstract: Autonomous medical and robotic systems increasingly rely on intelligent perception and reasoning capabilities to interpret visual data and support clinical decision making.

By Hamza Riaz, Arham Haroon, Maha Baig, Muhammad Dawood Rizwan, Muhammad Naseer Bajwa, Muhammad Moazam Fraz
arXiv AI
Jul 14

Towards Autonomous and Auditable Medical Imaging Model Development

arXiv:2607. 10522v1 Announce Type: cross Abstract: Large language model (LLM) agents are beginning to automate machine learning engineering (MLE) by coupling planning, code execution, debugging, and empirical feedback.

By Shengyuan Liu, Jia-Xuan Jiang, Boyun Zheng, Cheng Wang, Zipei Wang, Wentao Pan, Hongtao Wu, Houwen Peng, Yu Gu, Lichao Sun, Yixuan Yuan
Hugging Face Trending Papers
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Towards Autonomous and Auditable Medical Imaging Model Development

Large language model (LLM) agents are beginning to automate machine learning engineering (MLE) by coupling planning, code execution, debugging, and empirical feedback. Translating this capability to medical imaging remains difficult because each task imposes modality-specific experimentation and strict requirements for validation protocols and prediction artifacts.

arXiv AI
Jun 2

RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography

arXiv:2604. 15231v2 Announce Type: replace Abstract: Vision-language models (VLM) have markedly advanced AI-driven interpretation and reporting of complex medical imaging, such as computed tomography (CT).

By M\'elanie Roschewitz, Kenneth Styppa, Yitian Tao, Jiwoong Sohn, Jean-Benoit Delbrouck, Benjamin Gundersen, Nicolas Deperrois, Christian Bluethgen, Julia E. Vogt, Bjoern Menze, Farhad Nooralahzadeh, Michael Krauthammer, Michael Moor