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
CBIDR: A novel method for information retrieval combining image and data by means of TOPSIS applied to medical diagnosis
Humberto Giuri, Renato A. Krohling
UNICORN: A Deep Learning Model for Integrating Multi-Stain Data in Histopathology
Valentin Koch, Sabine Bauer, Valerio Luppberger, Michael Joner, Heribert Schunkert, Julia A. Schnabel, Moritz von Scheidt, Carsten Marr
Comparative Analysis of Transfer Learning Models for Breast Cancer Classification
Sania Eskandari, Ali Eslamian, Qiang Cheng
IBO: Inpainting-Based Occlusion to Enhance Explainable Artificial Intelligence Evaluation in Histopathology
Pardis Afshar, Sajjad Hashembeiki, Pouya Khani, Emad Fatemizadeh, Mohammad Hossein Rohban