Pathology Image

Pathology image analysis focuses on extracting meaningful information from digitized microscope slides to improve disease diagnosis and prognosis. Current research heavily utilizes deep learning, employing architectures like convolutional neural networks (CNNs), transformers, and variational autoencoders (VAEs), often incorporating techniques such as contrastive learning, multiple instance learning (MIL), and self-supervised learning to address challenges like limited labeled data and the inherent complexity of whole slide images. These advancements are significantly impacting healthcare by enabling faster, more accurate diagnoses, personalized treatment strategies, and potentially reducing the workload on pathologists, particularly for tasks like cancer grading and recurrence prediction.