AngioGraphCAD models coronary arteries as graphs, letting a graph neural network assess lesion-level risk the way cardiologists reason — one lesion at a time.
A team from Imperial College London, Royal Brompton Hospital, and TU Munich built a framework that combines variable CAIPIRINHA acquisition with artefact-aware dual-branch deep learning to achieve 18-fold acceleration in cardiac diffusion tensor imaging while cutting key biomarker errors by…
A team from Wuhan University of Science and Technology, Xiamen University, and the University of New South Wales built a multiple-magnification MIL framework that uses optimal transport to match tissue features across magnification levels, outperforming every tested baseline across four…
YOLO (You Only Look Once) has been the practical standard for real-time object detection since 2015. What started as a single-pass detector that traded some accuracy for dramatic speed has become a mature family of models, each generation narrowing the…
YOLOv11 Object Detection: From Zero to Deployment (2026 Guide) Read More »
Researchers at Central South University fine-tuned the Segment Anything Model 2 with full-parameter adaptation, a cross-scale feature fusion decoder, and a hybrid loss function — building a single model that achieves up to 98.13% mIoU across 13 material microscopy datasets…
SAMM: SAM2 Fine-Tuned for Universal Material Micrograph Segmentation Read More »
Researchers at Sichuan University of Science and Engineering built a network that fuses reverse attention, multi-scale criss-cross self-attention, and a Laplacian edge filter borrowed from classical image processing to delineate colorectal polyp boundaries with a precision and speed that most…
Researchers at Hohai University built a network that treats high-frequency spectral components — the sharp edges, fine contours, and subtle discontinuities that standard convolutions quietly destroy — as first-class semantic cues rather than noise to be filtered away, and the…
FreDNet: The Remote Sensing Segmenter That Learned to Hear the Image, Not Just See It Read More »
Breast cancer is, in raw numbers, among the most frequently diagnosed cancers in women worldwide — roughly one in four newly detected female cancer cases. The mortality statistics are grimmer still. What is easy to miss in those figures, though,…
Breast cancer remains the leading cause of cancer-related deaths among women worldwide, accounting for 15.5% of all female cancer fatalities according to 2024 global statistics. With incidence rates rising particularly in low and middle-income regions, the need for accurate, accessible…
In modern healthcare, multimodal medical data—combining imaging scans, electronic health records (EHR), genetic information, and physiological parameters—has emerged as the gold standard for accurate prognosis prediction. Studies consistently demonstrate that integrating diverse data types significantly improves diagnostic accuracy, model interpretability,…