A haematologist bent over a microscope is doing something quietly heroic. From a smear of blood, they pick out the one cell in a thousand that looks wrong, the immature blast that can be the first sign of leukaemia, and…
CytoDiffusion: A Generative Classifier for Blood Cell Morphology Read More »
University of Illinois researchers solved a decade-old open problem in photoacoustic breast imaging — how to reconstruct from hemispherical data without a closed-form formula — by training a strictly linear 3D U-Net to approximate the unknown filter, achieving iterative reconstruction…
Learned Half-Scan FBP: Physics-Informed Deep Learning for PACT Breast Imaging Read More »
Convolutional networks are good at spotting tumors, cataracts and the subtle atrophy patterns that show up in Alzheimer scans. They have been good at this for years. What they are not good at, on their own, is telling a physician…
Knowledge Distillation Helps Medical AI Explain Its Choices Read More »
Diffuse gliomas make up roughly 80 percent of malignant brain tumors and a large share of primary brain tumor deaths, and glioblastoma, the most aggressive subtype, still carries a median survival under 15 months after diagnosis despite decades of treatment…
A Diffusion Model That Paints What Your Glioma Will Look Like Next Year 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 »
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 »