Histology Image

Histology image analysis uses computational methods, primarily deep learning, to extract meaningful information from digitized tissue slides, aiming to improve diagnostic accuracy and accelerate research. Current research focuses on developing robust models for object detection, segmentation (especially of nuclei and glands), and gene expression prediction from images, often employing architectures like U-Nets, Transformers, and diffusion models, along with techniques like contrastive learning and self-supervised learning to address data limitations. These advancements hold significant potential for improving disease diagnosis, treatment planning, and biomarker discovery in various fields, including oncology and neuroscience, by automating tasks currently performed manually by pathologists.