Segmenting the internal structures in a CT or MRI scan, tracing the exact outline of a heart chamber, an organ, or a tumor, is essential groundwork for a long list of clinical applications, and most of the strong supervised segmentation…
How Bidirectional Copy-Paste Closes the Labeled Unlabeled Gap 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 »
Most deep learning systems for cancer grading are trained the same way an image classifier is trained to tell cats from dogs. Each tissue patch gets a single label, benign, well differentiated, moderately differentiated or poorly differentiated, and the network…
DIOR-VIT: Vision Transformers Learn the Order of Cancer Grades Read More »
Prostate cancer is, by the paper’s own framing, the second most frequently diagnosed cancer and the fifth leading cause of cancer death among men worldwide. Grading how aggressive a tumor looks under the microscope, a task pathologists perform using a…
Every convolutional network builds a stack of feature maps as an image moves through its layers. The early layers see fine texture, edges, small color patches, the kind of detail a dermatologist notices when zooming in on a lesion border.…
SPCB-Net And Skin Cancer Detection Explained Read More »
Knowledge distillation has been one of the most reliable tools for making a smaller, deployable model perform closer to a large, expensive one. The classic recipe trains a big teacher network first, then trains a compact student to mimic the…
A Single Model Can Now Teach Itself Through Patch Swaps Read More »
Super resolution ultrasound, also called ultrasound localization microscopy, works by injecting a patient or an animal with microbubble contrast agents and then watching where those bubbles go as blood carries them through the smallest vessels in an organ. Each bubble…
How Multi Frame Deconvolution Sharpens Super Resolution Ultrasound Read More »
Retinal fundus photographs are one of the more quietly useful tools in modern medicine. A single photograph of the back of the eye, taken through the pupil with no incision and no injection, can reveal signs of diabetic retinopathy, but…
Generative Models: A Causal Fix for Shortcut Learning in Retinal AI Models Read More »
Contrast enhanced imaging is genuinely useful, which is exactly why it became standard practice. In MRI, gadolinium based agents make the difference in signal between tumor tissue and normal tissue much easier to see, and in CT, iodinated contrast media…
A Contrast Free Tumor AI Owes Little to Its Text Model Read More »