SUMMARY An AI-driven system analyzes histopathology images of oral lesions using deep learning, self-supervised learning, and attention methods to predict cancer progression risk with explainable and uncertainty-quantified outputs that inform treatment decisions. The Unmet Need: Automated and accurate malignancy prediction tools The… Read More
SUMMARY Deep learning and uncertainty estimation (via Monte Carlo dropout and cross-validation) to classify digital pathology images by confidence, improving reliability in cancer diagnostics and supporting effective clinical decision-making The Unmet Need: Framework for enhancing reliability of deep learning model predictions in clinical… Read More
SUMMARY A deep learning system analyzes high-resolution pathology images to predict thyroid tumor subtypes and gene expression patterns, helping distinguish less aggressive cancers from those requiring more intensive treatment The Unmet Need: Reliable mechanisms for identifying thyroid tumor subtypes to safely de-escalate therapy… Read More
SUMMARY Deep learning tools are becoming increasingly popular for histologic analyses and have been used to predict tumor biomarker status, clinical variables, tumor subtypes, and mutation status in a variety of cancers. Accurate prediction of recurrence risk for post-operative cancer recurrence is of significant clinical interest as it can be… Read More