Medical Image Analysis

Medical image analysis develops methods to detect disease, segment anatomy, track changes over time, reconstruct images, and estimate clinically useful measurements from modalities such as CT, MRI, X-rays, ultrasound, and retinal imaging. Common approaches include U-Net-style segmentation, transformers, self-supervised and foundation-model learning, multimodal vision-language systems, and methods that incorporate anatomical or physical constraints. Models are tested on tasks and benchmarks such as BraTS for brain tumors, ACDC for cardiac MRI, and EchoNet for echocardiography, with growing attention to external-site testing, calibration, and clinical workflow. A central challenge is that strong benchmark results may not transfer across scanners, hospitals, patient groups, or acquisition protocols, so reliable deployment requires better validation, uncertainty estimates, and safeguards against bias and privacy risks.