Supervised learning aims to build a function or model that seeks as many map**s as
possible between the training data and outputs, where each training data will predict as a …

The human cerebral cortex undergoes dramatic and critical development during early
postnatal stages. Benefiting from advances in neuroimaging, many infant brain magnetic …

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) advancements, especially deep learning, have significantly
improved medical image processing and analysis in various tasks such as disease …

Establishing dense anatomical correspondence across distinct imaging modalities is a
foundational yet challenging procedure for numerous medical image analysis studies and …

Segment Anything Model (SAM) has achieved impressive results for natural image
segmentation with input prompts such as points and bounding boxes. Its success largely …

The idea of a systematic digital representation of the entire known human pathophysiology,
which we could call the Virtual Human Twin, has been around for decades. To date, most …

Deep learning (DL) models have provided state-of-the-art performance in various medical
imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) …

There are considerable interests in automatic stroke lesion segmentation on magnetic
resonance (MR) images in the medical imaging field, as stroke is an important …

The astounding success made by artificial intelligence in healthcare and other fields proves
that it can achieve human-like performance. However, success always comes with …