Machine learning identifies esophageal cancer better than current methods

Researchers have developed a deep learning model to accurately identify cancerous esophagus tissue on microscopy images instead of the high-cost, time-consuming manual annotation process used by pathologists. A research team at Dartmouth and Dartmouth-Hitchcock Norris Cotton Cancer Center tested their new machine learning approach for identifying cancerous and precancerous esophagus tissue on high-resolution microscopy images. Whole-slide images were collected from patients who underwent endoscopic esophagus and gastroesophageal junction mucosal biopsy, and an attention-based deep neural network framework was used to classify microscopy images. Results of the study were published on Wednesday in JAMA Network Open. "Previous methods for analyzing microscopy images were limited by bounding box annotations and unscalable heuristics," state the authors.