Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

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

In semi-supervised medical image segmentation, there exist empirical mismatch problems
between labeled and unlabeled data distribution. The knowledge learned from the labeled …

Abstract Despite that Convolutional Neural Networks (CNNs) have achieved promising
performance in many medical image segmentation tasks, they rely on a large set of labeled …

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

With the unprecedented developments in deep learning, automatic segmentation of main
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …

Automated segmentation in medical image analysis is a challenging task that requires a
large amount of manually labeled data. However, most existing learning-based approaches …

We propose a Transformer architecture for volumetric segmentation, a challenging task that
requires keeping a complex balance in encoding local and global spatial cues, and …

In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively
exploit the unlabeled data for semi-supervised medical image segmentation. The MC-Net+ …

Deep learning has led to tremendous progress in the field of medical artificial intelligence.
However, training deep-learning models usually require large amounts of annotated data …