Gleason Grade

Gleason grading is a crucial system for assessing prostate cancer severity, based on the microscopic appearance of tumor tissue. Current research heavily focuses on automating this process using deep learning, employing architectures like convolutional neural networks (ConvNeXt), Vision Transformers, and attention-based models to analyze histopathology images and even MRI scans for improved accuracy and efficiency. These advancements aim to reduce inter-observer variability, expedite diagnosis, and potentially improve patient outcomes by enabling more precise risk stratification and treatment planning. Furthermore, research explores using multimodal data (MRI and histopathology) and weakly supervised learning techniques to address data limitations and improve model generalizability.