Prostate Cancer Whole Slide Image

Prostate cancer whole slide image (WSI) analysis leverages digital pathology to improve diagnostic accuracy and efficiency. Current research focuses on developing and improving deep learning models, such as convolutional neural networks (CNNs) and autoencoders, to analyze these high-resolution images, often addressing challenges like computational cost and data scarcity through techniques like knowledge distillation and data augmentation. These advancements aim to aid pathologists in grading prostate cancer, predicting patient outcomes, and ultimately improving the diagnosis and treatment of this prevalent disease. The ultimate goal is to create reliable, automated systems that reduce diagnostic errors and improve the speed and consistency of prostate cancer assessment.