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 »
Medical imaging has always been a cornerstone of diagnostics, treatment planning, and disease monitoring. Among the most critical tasks in this field is 3D medical image segmentation , which enables precise delineation of anatomical structures and pathological regions in volumetric…
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 »
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 »
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 »
This article explains and critically evaluates a piece of published research. It is not medical advice, a diagnostic tool, or a treatment recommendation. Nothing here should inform an actual clinical decision. Readers with questions about a medical condition or an…
SVIS-RULEX Explains Medical AI Decisions, But Its 26 Features Have a Math Problem Read More »
Ask anyone who has tried to build a medical image segmentation model what the hardest part is, and the honest answer is rarely the neural network architecture. It is getting enough labeled data. A useful segmentation dataset needs pixel accurate…
DFCPS: Teaching a Polyp Detector With Barely Any Labels Read More »
Imagine training a heart scan segmentation model with exactly one labeled patient. That is not a hypothetical stress test, it is the actual one percent labeled setting a team from Zhejiang University and Ritsumeikan University put their method through, and…
SemSim Adds Semantic Similarity to FixMatch Segmentation Read More »