Deep learning has recently demonstrated considerable promise for a variety of computer
vision tasks. However, in many practical applications, large-scale labeled datasets are not …

Foundation models, large-scale, pre-trained deep-learning models adapted to a wide range
of downstream tasks have gained significant interest lately in various deep-learning …

Tumor synthesis enables the creation of artificial tumors in medical images facilitating the
training of AI models for tumor detection and segmentation. However success in tumor …

Recent advancements in foundation models have shown significant potential in medical
image analysis. However, there is still a gap in models specifically designed for medical …

Fairness is a critical concern in deep learning especially in healthcare where these models
influence diagnoses and treatment decisions. Although fairness has been investigated in the …

Abstract Self-Supervised Learning (SSL) has demonstrated promising results in 3D medical
image analysis. However the lack of high-level semantics in pre-training still heavily hinders …

We demonstrate that AI models can accurately segment liver tumors without the need for
manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two …

Foundation model, trained on a diverse range of data and adaptable to a myriad of tasks, is
advancing healthcare. It fosters the development of healthcare artificial intelligence (AI) …

We introduce the largest abdominal CT dataset (termed AbdomenAtlas) of 20,460 three-
dimensional CT volumes sourced from 112 hospitals across diverse populations …

The lack of annotated datasets is a major bottleneck for training new task-specific
supervised machine learning models, considering that manual annotation is extremely …