Universal Lesion Segmentation Challenge 2023: A Comparative Research of Different Algorithms

Medical image segmentation is a crucial task in medical image processing. Thanks to the advent of CNN[12], U-Net [17], and their variants such as V-Net[14], 3D U-Net[5], Res-UNet[15], Dense-UNet[13], we are able to perform segmentation task with precision. More recently, with implementations of transformer-based models, the medical imaging community enjoyed satisfying success in segmentation tasks. Networks like Medical Transformers[18] and SwinUnet[1] push the front-line boundary to another degree. Others have implemented learning methodologies from other fields, such as dictionary learning, to work on medical images. KEN[16] - knowledge embedding network - for example, takes advantage of the fruitfulness of information embedding in each layer via dictionary learning to provide a more semantically meaningful network.