Yuhao Huang

Achieving high-accuracy landmark localization in medical imaging without the computational burden of traditional multi-stage methods could revolutionize clinical measurement practices.

Tail classes in medical image classification can be effectively shielded from head-class encroachment, leading to significant improvements in performance.

ClinRAG-GRAPH achieves impressive pCR prediction accuracy while ensuring interpretability and robustness against imaging biases across multiple centers.

FrameONE revolutionizes echocardiographic keyframe detection by achieving state-of-the-art accuracy through a unified multi-view approach that overcomes traditional limitations.

Training-free anomaly classification in prenatal ultrasound can now be achieved with just a few reference images, revolutionizing diagnostic accessibility.

Achieving a mean absolute error of just 161.3 g, this method revolutionizes fetal birth weight estimation by eliminating the need for expert operators in ultrasound assessments.