Histopathological Image
Histopathological image analysis focuses on extracting meaningful information from microscopic images of tissue samples, primarily to aid in disease diagnosis and prognosis. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), transformers, and graph neural networks (GNNs) for tasks like image segmentation, classification, and multimodal data integration (e.g., combining H&E and immunofluorescence images, or integrating genomic data). These advancements are significantly impacting healthcare by improving diagnostic accuracy, accelerating workflows, and potentially enabling more personalized medicine through improved prediction of treatment response and disease progression.
Papers
Deep Neural Networks integrating genomics and histopathological images for predicting stages and survival time-to-event in colon cancer
Olalekan Ogundipe, Zeyneb Kurt, Wai Lok Woo
Mixed Supervision of Histopathology Improves Prostate Cancer Classification from MRI
Abhejit Rajagopal, Antonio C. Westphalen, Nathan Velarde, Tim Ullrich, Jeffry P. Simko, Hao Nguyen, Thomas A. Hope, Peder E. Z. Larson, Kirti Magudia