2.3M 슬라이드로 학습된 PRISM2, 픽셀 대신 진단을 생성하다
paige와 microsoft가 2.3m 슬라이드와 임상 보고서로 학습되어 진단 텍스트를 생성하는 병리 모델 prism2를 공개했습니다. 이는 병리학을 이미지 분류에서 해석 가능한 임상 대화로 전환시킵니다.
paige and microsoft introduced prism2, a perceiver-based encoder trained jointly on tissue tiles and pathology reports to interpret whole-slide images and answer diagnostic questions. the model aggregates thousands of tile embeddings per slide from a dataset of 2.3 million slides to generate text-based conclusions. this moves beyond standard image classification by integrating clinical context into the diagnostic loop. it signals a maturation of medical ai from simple detection tools to systems capable of complex reasoning and communication.