Tag: digital pathology

A new review finds AI-derived biomarkers show promising, though variable, performance in predicting cancer immunotherapy response across tumor types.

A retrospective study in Diagnostic Pathology found that a biopsy-trained AI algorithm achieved 94.3% sensitivity for detecting incidental prostate adenocarcinoma ...

Veterinary researchers evaluated automated nuclei quantification across experimental and forensic porcine skin wounds, finding age-related density declines that vary significantly ...

A new deep learning framework called BigpictureQC automates the detection of artifacts in digital pathology slides, identifying poor-quality images with ...

A systematic review examines how deep learning algorithms analyze gigapixel whole-slide images to classify tumors and predict transcriptomic profiles directly ...

A new review maps the opportunities and obstacles facing digital pathology and AI adoption in Saudi Arabia under Vision 2030.

A deep learning model analyzing diagnostic biopsies accurately predicts whether esophageal adenocarcinoma patients will respond to preoperative radiochemotherapy.

An interpretable pathomics framework predicted BRAF V600E status in papillary thyroid carcinoma from routine Wright-stained cytology slides, reaching an AUC ...

AI assistance raised prostate biopsy diagnostic accuracy among pathology residents, with the largest gains among less experienced trainees and on ...

A validated HALO AI digital pathology pipeline automatically distinguishes CAA-positive vessels from β-amyloid plaques and shows that higher CAA scores ...