Abstract The Segment Anything Model (SAM) is the first foundation model for general image
segmentation. It has achieved impressive results on various natural image segmentation …

Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to
a readily accessible tool for cancer researchers. AI-based tools can boost research …

Hematoxylin-and eosin-stained whole-slide images (WSIs) are the foundation of diagnosis
of cancer. In recent years, development of deep learning-based methods in computational …

The release of nnU-Net marked a paradigm shift in 3D medical image segmentation,
demonstrating that a properly configured U-Net architecture could still achieve state-of-the …

Owing to the success of transformer models, recent works study their applicability in 3D
medical segmentation tasks. Within the transformer models, the self-attention mechanism is …

Recent advances in generative AI have brought incredible breakthroughs in several areas,
including medical imaging. These generative models have tremendous potential not only to …

Federated learning (FL) enables building robust and generalizable AI models by leveraging
diverse datasets from multiple collaborators without centralizing the data. We created …

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of
radiation to tumors while sparing healthy tissues over multiple days. Computed tomography …

Foundation models in deep learning are characterized by a single large-scale model trained
on vast amounts of data serving as the foundation for various downstream tasks. Foundation …

The Segment Anything Model (SAM) is a new image segmentation tool trained with the
largest available segmentation dataset. The model has demonstrated that, with prompts, it …