[HTML][HTML] Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis
The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …
respect to the quantity of high-performing solutions reported in the literature. End users are …
[HTML][HTML] A review of uncertainty estimation and its application in medical imaging
The use of AI systems in healthcare for the early screening of diseases is of great clinical
importance. Deep learning has shown great promise in medical imaging, but the reliability …
importance. Deep learning has shown great promise in medical imaging, but the reliability …
Assessing reliability and challenges of uncertainty estimations for medical image segmentation
Despite the recent improvements in overall accuracy, deep learning systems still exhibit low
levels of robustness. Detecting possible failures is critical for a successful clinical integration …
levels of robustness. Detecting possible failures is critical for a successful clinical integration …
Estimating uncertainty in deep learning for reporting confidence to clinicians in medical image segmentation and diseases detection
Deep learning (DL), which involves powerful black box predictors, has achieved a
remarkable performance in medical image analysis, such as segmentation and classification …
remarkable performance in medical image analysis, such as segmentation and classification …
Leveraging uncertainty estimates for predicting segmentation quality
The use of deep learning for medical imaging has seen tremendous growth in the research
community. One reason for the slow uptake of these systems in the clinical setting is that …
community. One reason for the slow uptake of these systems in the clinical setting is that …
[HTML][HTML] AUQantO: Actionable Uncertainty Quantification Optimization in deep learning architectures for medical image classification
Deep learning algorithms have the potential to automate the examination of medical images
obtained in clinical practice. Using digitized medical images, convolution neural networks …
obtained in clinical practice. Using digitized medical images, convolution neural networks …
Layer ensembles: A single-pass uncertainty estimation in deep learning for segmentation
Uncertainty estimation in deep learning has become a leading research field in medical
image analysis due to the need for safe utilisation of AI algorithms in clinical practice. Most …
image analysis due to the need for safe utilisation of AI algorithms in clinical practice. Most …
A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods
The comprehensive integration of machine learning healthcare models within clinical
practice remains suboptimal, notwithstanding the proliferation of high-performing solutions …
practice remains suboptimal, notwithstanding the proliferation of high-performing solutions …
[HTML][HTML] Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation
T Buddenkotte, LE Sanchez, M Crispin-Ortuzar… - Computers in Biology …, 2023 - Elsevier
Uncertainty quantification in automated image analysis is highly desired in many
applications. Typically, machine learning models in classification or segmentation are only …
applications. Typically, machine learning models in classification or segmentation are only …
Uncertainty quantification and estimation in medical image classification
S Yang, T Fevens - Artificial Neural Networks and Machine Learning …, 2021 - Springer
Abstract Deep Neural Networks (DNNs) have shown tremendous success in numerous AI-
related fields. However, despite DNNs exhibiting remarkable performance, they still can …
related fields. However, despite DNNs exhibiting remarkable performance, they still can …