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) …

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 …

The universal model emerges as a promising trend for medical image segmentation, paving
up the way to build medical imaging large model (MILM). One popular strategy to build …

Deep learning has seen rapid growth in recent years and achieved state-of-the-art
performance in a wide range of applications. However, training models typically requires …

One-shot medical image segmentation (MIS) aims to cope with the expensive time-
consuming and inherent human bias annotations. One prevalent method to address one …

The Segment Anything Model (SAM) is a foundational model that has demonstrated
impressive results in the field of natural image segmentation. However, its performance …

Self-supervised learning (SSL) has long had great success in advancing the field of
annotation-efficient learning. However, when applied to CT volume segmentation, most SSL …

The foundation models based on pre-training technology have significantly advanced
artificial intelligence from theoretical to practical applications. These models have facilitated …

Interpretability for machine learning models in medical imaging (MLMI) is an important
direction of research. However, there is a general sense of murkiness in what interpretability …

The Vision Transformer (ViT) has demonstrated remarkable performance in Self-Supervised
Learning (SSL) for 3D medical image analysis. Masked AutoEncoder (MAE) for feature pre …