Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

Denoising diffusion models, a class of generative models, have garnered immense interest
lately in various deep-learning problems. A diffusion probabilistic model defines a forward …

Abstract We introduce the Segment Anything (SA) project: a new task, model, and dataset for
image segmentation. Using our efficient model in a data collection loop, we built the largest …

The Segment Anything Model (SAM) has recently gained popularity in the field of image
segmentation due to its impressive capabilities in various segmentation tasks and its prompt …

Semantic segmentation of brain tumors is a fundamental medical image analysis task
involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient …

The landscape of publicly available vision foundation models (VFMs) such as CLIP and
SAM is expanding rapidly. VFMs are endowed with distinct capabilities stemming from their …

Although machine learning (ML) has shown promise across disciplines, out-of-sample
generalizability is concerning. This is currently addressed by sharing multi-site data, but …

Abstract Diffusion Probabilistic Model (DPM) has recently become one of the hottest topics in
computer vision. Its image generation applications, such as Imagen, Latent Diffusion …

Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

Brain tumor semantic segmentation is a critical medical image processing work, which aids
clinicians in diagnosing patients and determining the extent of lesions. Convolutional neural …