ViM-UNet, a novel segmentation architecture based on the Vision Mamba (ViM) architecture, performs similarly or better than the popular UNet model and outperforms the UNETR transformer-based model, while being more computationally efficient.
Generative diffusion models can be used to simulate dataset shifts and diagnose failure modes of biomedical vision models, without additional data collection, by performing targeted image editing.
Attention-based models can effectively replace computationally complex convolutional neural networks (CNNs) for biomedical image analysis by capturing long-range dependencies and introducing locality through novel techniques like Shifted Patch Tokenization (S.P.T.) and Lancoz5 interpolation.