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
Test Time Transform Prediction for Open Set Histopathological Image Recognition
Adrian Galdran, Katherine J. Hewitt, Narmin L. Ghaffari, Jakob N. Kather, Gustavo Carneiro, Miguel A. González Ballester
Test-time image-to-image translation ensembling improves out-of-distribution generalization in histopathology
Marin Scalbert, Maria Vakalopoulou, Florent Couzinié-Devy
FHIST: A Benchmark for Few-shot Classification of Histological Images
Fereshteh Shakeri, Malik Boudiaf, Sina Mohammadi, Ivaxi Sheth, Mohammad Havaei, Ismail Ben Ayed, Samira Ebrahimi Kahou
Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images
Jun Shi, Yuanming Zhang, Zheng Li, Xiangmin Han, Saisai Ding, Jun Wang, Shihui Ying