Tag: computational pathology

Researchers report that a data-efficient vision transformer classified lung cancer histopathology images with 98.64 percent accuracy and near-perfect AUC while ...

Researchers have developed a frequency-decoupled AI framework that nearly doubles cross-center nuclei segmentation accuracy in histopathology without requiring stain normalization ...

Researchers combined a vision transformer with a convolutional network and tuned the pair using whale- and swarm-inspired algorithms, reaching 96.27 ...

Researchers developed RIDGE, a weakly supervised deep learning system that predicts gene expression signatures and biomarkers such as microsatellite instability ...

Researchers in China have developed a neighbor-constrained attention framework that improves how artificial intelligence classifies gigapixel whole-slide pathology images by ...

Researchers have developed SlideChat, a multimodal generative AI assistant that interprets gigapixel whole-slide pathology images across 31 cancer types and ...

Researchers have developed BasNet, an open-source attention U-Net that segments axons in Bielschowsky silver-stained brain sections with accuracy that meets ...

Researchers have developed ReliFuse, a machine learning framework that fuses cached predictions from multiple segmentation experts to segment lung vessels ...

A new study is stirring excitement across computational pathology, as researchers evaluate how well today’s “vision and pathology foundation models” ...