With the development of 5G and Internet of Things (IoT), the era of big data‐driven product
design is booming. In addition, artificial intelligence (AI) is also emerging and evolving by …

An increasing number of public datasets have shown a marked impact on automated organ
segmentation and tumor detection. However, due to the small size and partially labeled …

Multiparametric magnetic resonance imaging (mpMRI) is an indispensable tool in the
clinical workflow for the diagnosis and treatment planning of various diseases. Machine …

In this paper, we introduce a unified and generalist Biomedical Generative Pre-trained
Transformer (BiomedGPT) model, which leverages self-supervision on large and diverse …

Accurate and massive medical image annotation data is crucial for diagnosis, surgical
planning, and deep learning in the development of medical images. However, creating large …

Accurate medical image segmentation demands the integration of multi-scale information,
spanning from local features to global dependencies. However, it is challenging for existing …

Abstract Background and Objective: Open-source deep learning toolkits are one of the
driving forces for developing medical image segmentation models that are essential for …

Which volume to annotate next is a challenging problem in building medical imaging
datasets for deep learning. One of the promising methods to approach this question is active …

High-resolution X-ray computed tomography (micro-CT) has been widely used to
characterise fluid flow in porous media for different applications, including in gas diffusion …

Multimodal MR brain tumor segmentation is one of the hottest issues in the community of
medical image processing. However, acquiring the complete set of MR modalities is not …