Brain foundation models have shown promising advances in learning generalizable representations from functional magnetic resonance imaging (fMRI) data. However, existing models are typically tied to specific brain atlases and cannot be applied to fMRI data represented using different atlases without full retraining, th...
Thyroid ultrasound diagnosis requires coordinated lesion localization, measurement, risk stratification and reporting, yet most AI systems address these tasks in isolation and provide limited support for clinical review. We present ThyroidXAgent, a clinician-interactive agentic AI system that coordinates specialized di...
Haifan Gong, Shiyu Chen, Bodong Wang et al.· 0 citations
This work presents $\Phi$-Bench, a benchmark for systematically evaluating LLMs on engineering the LLM infrastructure stack and spans tasks of varying complexity, ranging from localized kernel-level function completion to long-horizon implementation and end-to-end system optimization.
Lei-Lei Ding, Shu-Min Wang, Yu-Ting Huang et al.· 1 citation
Computed tomography (CT) vision-language pretraining from paired volumes and radiology reports is a scalable yet challenging task. Existing methods commonly adopt global scan-report contrast, which is scalable but obscures heterogeneous organ evidence. Meanwhile, direct organ-level alignment remains coarse, since the s...
OKA-CT is proposed, an organ-hierarchical knowledge-augmented framework for CT-report VLP that achieves zero-shot abnormality diagnosis AUROCs on CT-RATE and RAD-ChestCT datasets and shows improved report-image alignment and stronger sensitivity to disease-associated anatomical regions.
Volumetric CT vision-language pretraining learns 3D representations from scan-report pairs, but global and anatomy-aware objectives supervise only correspondence: they establish what is present and leave how much unconstrained. Nothing separates a mild from an extensive case of the same finding along a consistent direc...
