Researchers validated a disease-specific deep learning architecture that accurately automates metabolic tumor volume segmentation in Hodgkin lymphoma positron-emission tomography scans.

As large medical imaging datasets become widely available, researchers are increasingly turning to artificial intelligence to extract useful information from scans that were never manually annotated.

UAC-Net combined contextual attention and multi-scale feature fusion in a shared neural network that classified three brain-tumor types and delineated their MRI boundaries on two public datasets.

A research team from South Korea’s Jeju National University has developed a novel deep learning PV segmentation technique for remote sensing images. Called the Solar-Mamba, the novel method is a ...

Example of segmentation visualization in the CrossSegmentationExplorer system. After selecting the image volume, segmentation models or files are chosen from the drop-down menus. For each selected ...