Despite the considerable progress in automatic abdominal multi-organ segmentation from
CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is …

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 …

While deep learning models have become the predominant method for medical image
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …

Recently, deep learning with Convolutional Neural Networks (CNNs) and Transformers has
shown encouraging results in fully supervised medical image segmentation. However, it is …

Biomedical image segmentation is a crucial part of both scientific research and clinical care.
With enough labelled data, deep learning models can be trained to accurately automate …

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

AI for cancer detection encounters the bottleneck of data scarcity, annotation difficulty, and
low prevalence of early tumors. Tumor synthesis seeks to create artificial tumors in medical …

The pre-training and fine-tuning paradigm has become prominent in transfer learning. For
example, if the model is pre-trained on ImageNet and then fine-tuned to PASCAL, it can …

How can we test AI performance? This question seems trivial, but it isn't. Standard
benchmarks often have problems such as in-distribution and small-size test sets …

Semantic medical image segmentation is a crucial part of both scientific research and
clinical care. With enough labelled data, deep learning models can be trained to accurately …