AutoPET-III
PulseAugur coverage of AutoPET-III — every cluster mentioning AutoPET-III across labs, papers, and developer communities, ranked by signal.
-
AI model enhances PET/CT lesion segmentation with interactive scribbles
Researchers have developed a novel interactive lesion segmentation method for PET/CT scans, utilizing a scribble-conditioned ResEnc U-Net. This approach leverages user-provided scribbles to mark foreground and backgroun…
-
New FEEDS strategy boosts AI cancer detection efficiency with reduced labeling
Researchers have developed a novel strategy called FEEDS (Foundation model-Enabled Efficient Data Sampling) to improve the efficiency of training AI models for cancer detection in PET/CT imaging. This method leverages v…
-
AI improves cancer lesion segmentation with uncertainty quantification
Researchers have developed a new framework to improve the segmentation of lesions in whole-body PET/CT scans for cancer staging. This approach integrates Bayesian ensembling to reduce variability and quantifies uncertai…