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Shenzhen University, Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Harvard Medical School
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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.
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.
FrameONE revolutionizes echocardiographic keyframe detection by achieving state-of-the-art accuracy through a unified multi-view approach that overcomes traditional limitations.
ClinRAG-GRAPH achieves impressive pCR prediction accuracy while ensuring interpretability and robustness against imaging biases across multiple centers.
