Medical AI brings together our reporting on machine learning for clinical and biomedical problems, from diagnosis and prognosis to medical image analysis and decision support. Because mistakes in this domain carry a human cost, we pay close attention to evaluation, calibration, uncertainty, and the gap between benchmark numbers and bedside reliability. Expect grounded explainers of recent research rather than uncritical product announcements.
Researchers at Sichuan University of Science and Engineering built a network that fuses reverse attention, multi-scale criss-cross self-attention, and a Laplacian edge filter borrowed from classical image processing to delineate colorectal polyp boundaries with a precision and speed that most…
ESM-AnatTractNet integrates electrophysiological validation with anatomical context to accurately classify true positive white matter pathways — enabling safer pediatric epilepsy surgery through precise, non-invasive functional mapping.
A compact temporal attention module called TAM quietly outperforms much heavier architectures on cardiac segmentation — by making existing networks motion-aware without rebuilding them from scratch.
TAM: Plug-and-Play Temporal Attention Module for Motion-Guided Cardiac Segmentation Read More »
Breast cancer is, in raw numbers, among the most frequently diagnosed cancers in women worldwide — roughly one in four newly detected female cancer cases. The mortality statistics are grimmer still. What is easy to miss in those figures, though,…
Primary liver cancer stands as the third leading cause of cancer-related deaths worldwide, claiming hundreds of thousands of lives annually. Despite advances in medical imaging, diagnosing the three distinct subtypes—hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and the rare combined hepatocellular-cholangiocarcinoma…
M2CR: Revolutionizing Primary Liver Cancer Diagnosis with AI-Powered Multimodal Analysis Read More »
Every cortex is folded in its own particular way. The overall pattern of ridges and grooves, called gyri and sulci, is recognizable from one person to the next, but the exact shape, depth, and location of any given fold varies…
GeoMorph Registers Brain Surfaces Using Deep Learning Read More »
A surgeon is halfway through an operation on a patient with a solid tumor. A frozen section goes to the pathologist, who reads it within minutes and tells the team whether the margin is clear. The slide that usually drives…
M3Surv Cancer Survival Prediction With Missing Data Read More »
Give a neurologist a brain scan from today and ask what it will look like in five years, and you are asking for something the field has wanted for a long time. Not a population average, not a risk score,…
latent diffusion model: Predicting Alzheimer’s Brain MRI Change With BrLP Read More »
U-Net has been the default architecture for medical image segmentation for close to a decade, built around a symmetric encoder decoder shape with skip connections that let fine detail from early layers reach the final output. It works because convolutions…
TransUNet And Where Transformers Help Medical Segmentation Read More »
Accurately outlining a lesion or pathology in a medical image, a mole on skin, a nodule on a thyroid ultrasound, a tumor on a brain MRI, is genuinely difficult work, and relying purely on expert visual judgment can be slow…
TBConvL-Net Pairs Swin Transformers With ConvLSTM for Segmentation Read More »