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.
Skin cancer is common enough that in the United States alone it affects roughly three million people every year, and melanoma, one of its three primary forms alongside basal cell carcinoma and squamous cell carcinoma, is particularly dangerous because it…
How EFAM-Net Reads Skin Lesions With ConvNeXt Attention Blocks Read More »
This article explains a piece of published neuroscience and engineering research. It is not medical advice, diagnosis, or treatment.
TACAF Fuses EEG And Breathing To Decode Smell Response Read More »
A microscopist leans over a thick blood film under a 100 times objective. The field is a wash of pale purple, and scattered through it are dozens of small dark discs. Some are Plasmodium parasites. Others are stain debris, platelets,…
Malaria Parasite Detection Using Uncertainty Attention Read More »
Motor imagery research has a long history, and most of it points at the hands. You imagine gripping something, a classifier tries to figure out which hand you meant, and there are entire competition datasets built around exactly that setup.…
How a Transformer MSC-T3AM Learns to Tell Your Left Leg From Your Right on EEG Read More »
Breast cancer statistics have a way of staying grim no matter how many times they get repeated. The World Health Organization logged over two million new cases worldwide in 2020 and close to seven hundred thousand deaths in the same…
How Texture and Shape Features Sharpen Breast Cancer AI Read More »
This article explains a piece of published research. It is not medical advice, diagnosis, or treatment.
Self Paced Multi Scale AI For Diabetic Retinopathy Grading Read More »
Every echocardiogram report that lists an ejection fraction, a chamber volume, or a wall thickness represents a human being drawing a line around a moving, low contrast, occasionally noisy structure on ultrasound. Clinical guidelines from the American Society of Echocardiography…
Self Supervised Learning: How AI Learned to Read Heart Ultrasounds Without Human Labels Read More »
Deep learning segmentation models are, by now, quite good at their job when they have enough data to learn from. The actual constraint most teams run into is upstream of the model entirely, getting enough pixel level annotated examples to…
GenSeg And Training Medical AI With Barely Any Data Read More »
Medical image segmentation has a labeling problem that computer vision in general does not face nearly as badly. Drawing a box around a cat in a photo takes seconds. Tracing the exact boundary of a pancreas across dozens of CT…
SDCL: Two Students Learning From Each Other’s Mistakes Fix a Blind Spot in Segmentation Read More »
Two brain MRI scans arrive in the same batch. One is crisp, the tumor margin clean against healthy tissue, and any decent model would call it correctly. The other is smeared by motion, low in contrast, the boundary a guess.…
Context Aware Adaptive Knowledge Distillation for Tumor Detection Read More »