Colonoscopy Data

Colonoscopy data analysis is a rapidly evolving field focused on improving the accuracy and efficiency of colorectal cancer screening. Current research emphasizes developing robust computer vision models, including deep learning architectures like GANs and Transformers, for tasks such as polyp detection and segmentation, 3D colon reconstruction, and real-time navigation assistance during procedures. These advancements aim to address limitations in current colonoscopy practices, such as inconsistent polyp detection rates and incomplete colon visualization, ultimately leading to improved diagnostic accuracy and patient outcomes. The development of large, annotated datasets and innovative data augmentation techniques are crucial for training and validating these models.