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
Graph Neural Networks in Histopathology: Emerging Trends and Future Directions
Siemen Brussee, Giorgio Buzzanca, Anne M. R. Schrader, Jesper Kers
Learned Image Compression for HE-stained Histopathological Images via Stain Deconvolution
Maximilian Fischer, Peter Neher, Tassilo Wald, Silvia Dias Almeida, Shuhan Xiao, Peter Schüffler, Rickmer Braren, Michael Götz, Alexander Muckenhuber, Jens Kleesiek, Marco Nolden, Klaus Maier-Hein
Exploring Explainable AI Techniques for Improved Interpretability in Lung and Colon Cancer Classification
Mukaffi Bin Moin, Fatema Tuj Johora Faria, Swarnajit Saha, Busra Kamal Rafa, Mohammad Shafiul Alam
Breast Histopathology Image Retrieval by Attention-based Adversarially Regularized Variational Graph Autoencoder with Contrastive Learning-Based Feature Extraction
Nematollah Saeidi, Hossein Karshenas, Bijan Shoushtarian, Sepideh Hatamikia, Ramona Woitek, Amirreza Mahbod