[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been�…
uncertainties during both optimization and decision making processes. They have been�…
Machine learning methods for small data challenges in molecular science
B Dou, Z Zhu, E Merkurjev, L Ke, L Chen…�- Chemical�…, 2023 - ACS Publications
Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in�…
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in�…
Unetr: Transformers for 3d medical image segmentation
Abstract Fully Convolutional Neural Networks (FCNNs) with contracting and expanding
paths have shown prominence for the majority of medical image segmentation applications�…
paths have shown prominence for the majority of medical image segmentation applications�…
[HTML][HTML] The medical segmentation decathlon
International challenges have become the de facto standard for comparative assessment of
image analysis algorithms. Although segmentation is the most widely investigated medical�…
image analysis algorithms. Although segmentation is the most widely investigated medical�…
In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection framework for semi-supervised learning
The recent research in semi-supervised learning (SSL) is mostly dominated by consistency
regularization based methods which achieve strong performance. However, they heavily�…
regularization based methods which achieve strong performance. However, they heavily�…
Semi-supervised medical image segmentation through dual-task consistency
Deep learning-based semi-supervised learning (SSL) algorithms have led to promising
results in medical images segmentation and can alleviate doctors' expensive annotations by�…
results in medical images segmentation and can alleviate doctors' expensive annotations by�…
Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
The medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new�…
segmentation models based on convolutional neural networks. Despite the new�…
A survey on active learning and human-in-the-loop deep learning for medical image analysis
Fully automatic deep learning has become the state-of-the-art technique for many tasks
including image acquisition, analysis and interpretation, and for the extraction of clinically�…
including image acquisition, analysis and interpretation, and for the extraction of clinically�…
Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation
Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually�…
clinical approaches. Recent success of deep learning-based segmentation methods usually�…
Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan
The recent outbreak of Coronavirus Disease 2019 (COVID-19) has led to urgent needs for
reliable diagnosis and management of SARS-CoV-2 infection. The current guideline is using�…
reliable diagnosis and management of SARS-CoV-2 infection. The current guideline is using�…