[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 …
Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice
Technological advancements have enabled the extended investigation, development, and
application of computational approaches in various domains, including health care. A …
application of computational approaches in various domains, including health care. A …
Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation
Despite recent advances in the accuracy of brain tumor segmentation, the results still suffer
from low reliability and robustness. Uncertainty estimation is an efficient solution to this …
from low reliability and robustness. Uncertainty estimation is an efficient solution to this …
CReg-KD: Model refinement via confidence regularized knowledge distillation for brain imaging
One of the core challenges of deep learning in medical image analysis is data insufficiency,
especially for 3D brain imaging, which may lead to model over-fitting and poor …
especially for 3D brain imaging, which may lead to model over-fitting and poor …
Cross-domain attention-guided generative data augmentation for medical image analysis with limited data
Data augmentation is widely applied to medical image analysis tasks in limited datasets with
imbalanced classes and insufficient annotations. However, traditional augmentation …
imbalanced classes and insufficient annotations. However, traditional augmentation …
Anatomically-aware uncertainty for semi-supervised image segmentation
Semi-supervised learning relaxes the need of large pixel-wise labeled datasets for image
segmentation by leveraging unlabeled data. A prominent way to exploit unlabeled data is to …
segmentation by leveraging unlabeled data. A prominent way to exploit unlabeled data is to …
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 …
A hybrid attention-based residual Unet for semantic segmentation of brain tumor
Segmenting brain tumors in Magnetic Resonance Imaging (MRI) volumes is challenging
due to their diffuse and irregular shapes. Recently, 2D and 3D deep neural networks have …
due to their diffuse and irregular shapes. Recently, 2D and 3D deep neural networks have …
Evaluating the fairness of deep learning uncertainty estimates in medical image analysis
Although deep learning (DL) models have shown great success in many medical image
analysis tasks, deployment of the resulting models into real clinical contexts requires:(1) that …
analysis tasks, deployment of the resulting models into real clinical contexts requires:(1) that …
Attention-guided multiple instance learning for COPD identification: To combine the intensity and morphology
Chronic obstructive pulmonary disease (COPD) is a complex and multi-component
respiratory disease. Computed tomography (CT) images can characterize lesions in COPD …
respiratory disease. Computed tomography (CT) images can characterize lesions in COPD …