Simultaneous classification and delineation of seven types of histological growth patterns in lung adenocarcinomas using self-supervised learning and online hard patch mining

The experimental findings demonstrate that the proposed framework achieves high accuracy in classifying normal lung tissues and seven types of lung tumor growth patterns, with its performance across all evaluation metrics rivaling that of pathologists' assessments. This model is capable of generating predictive probability maps at the level of full-slice lung images. These maps effectively visualize the location, distribution, and proportion of lesion areas, thereby facilitating pathologists in conducting further grading and staging tasks. Moreover, when the model's classification results are integrated with pathologists' diagnostic outcomes, the accuracy and reliability of the pathologists' clinical interpretations can be significantly enhanced. Besides, Hematoxylin and eosin staining and standardization measures ensure the method's generalizability across institutions or scanners.