Polyp Datasets

Polyp datasets are collections of images and videos from colonoscopies, used to train and evaluate computer vision models for automated polyp detection and segmentation, crucial for early colorectal cancer diagnosis. Current research focuses on improving the accuracy and speed of these models using various deep learning architectures, including transformers, convolutional neural networks (CNNs), and hybrid approaches that combine both, often incorporating techniques like attention mechanisms and multi-scale feature fusion. The development of larger, more diverse datasets, along with advancements in model design, aims to reduce the high miss rates currently associated with manual polyp detection during colonoscopies, ultimately improving patient outcomes.