SLIP: Interactive 3D Segmentation Needs Latency and Undo
SLIP caches 3D patch features and undoes clicks with 140 MB deltas. Cross-dataset click latency is 0.06 s versus 0.31 s for nnInteractive. MRI uterus still goes to nnInteractive.
CT-DeltaBench: Prior vs Current CT Comparison Is Still Unsolved
CT-ΔBench scores prior-vs-current CT difference reports on 2,638/169 CT-RATE pairs. Zero-shot Change-F1 tops out at 0.0175; DeltaMed full SFT reaches 0.1980.
AMPLIFAI: Feature-Level Masks on Multiphase CT for LI-RADS Liver AI
AMPLIFAI adds LI-RADS categories plus voxel masks for APHE, washout, and capsule on 590 multiphase CTs, so liver AI can show the features in a viewer.
RadYOLO: Why Fast 3D Detection, Not Just Dense Segmentation, Changes What Radiology AI Can Ship
RadYOLO is a 3D YOLO11 for joint detection and instance segmentation on CT and MRI. It is 8-46x faster than nnU-Net on GPU and stronger on lesions.
PatchChestCT: Why Spatial Labels, Not Image-Level Findings, Are the Missing Piece for Chest CT AI
PatchChestCT adds physician-reviewed 3D patch labels for nine chest CT findings on 2,201 CT-RATE studies, so models can localize, not just classify.
Dice vs Dice Loss
The dice loss in a special case of the dice coefficient but physically they are the same. Read this arrticle for more details
Federated learning in healthcare
Explore how federated learning in healthcare is creating a new era of medical AI, enabling collaborative innovation while protecting patient privacy.
Gaussian smoothing filter
Discover the smoothing Gaussian filter. This guide explains how it clarifies data, reduces noise in images, and powers innovation in science and medicine.
What is sensitivity and specificity
Learn what is sensitivity and specificity and how these metrics impact diagnostic accuracy. Quick examples and practical AI tips.
Convolutional neural networks explained
Learn convolutional neural networks explained in plain terms: discover how CNNs detect features with simple visuals and real-world examples.