Colorectal Cancer

Colorectal cancer research focuses on improving early detection and diagnosis, primarily through advanced image analysis techniques applied to various imaging modalities (e.g., colonoscopy, CT scans, histopathology). Current research employs deep learning models, including convolutional neural networks (CNNs), transformers, and graph convolutional networks (GCNs), often incorporating techniques like self-supervised learning, transfer learning, and multi-task learning to enhance accuracy and robustness across diverse datasets and imaging conditions. These advancements aim to improve the speed and accuracy of diagnosis, potentially leading to earlier interventions and improved patient outcomes, while also addressing challenges like data scarcity and variability in image quality.