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[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 …
[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 …
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge
Gliomas are the most common primary brain malignancies, with different degrees of
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie …
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie …
Deep learning based brain tumor segmentation: a survey
Brain tumor segmentation is one of the most challenging problems in medical image
analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain …
analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain …
Cross-modality deep feature learning for brain tumor segmentation
Recent advances in machine learning and prevalence of digital medical images have
opened up an opportunity to address the challenging brain tumor segmentation (BTS) task …
opened up an opportunity to address the challenging brain tumor segmentation (BTS) task …
Automatic brain tumor segmentation based on cascaded convolutional neural networks with uncertainty estimation
Automatic segmentation of brain tumors from medical images is important for clinical
assessment and treatment planning of brain tumors. Recent years have seen an increasing …
assessment and treatment planning of brain tumors. Recent years have seen an increasing …
Survey of dropout methods for deep neural networks
Dropout methods are a family of stochastic techniques used in neural network training or
inference that have generated significant research interest and are widely used in practice …
inference that have generated significant research interest and are widely used in practice …
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 …
Attention-guided version of 2D UNet for automatic brain tumor segmentation
Gliomas are the most common and aggressive among brain tumors, which cause a short life
expectancy in their highest grade. Therefore, treatment assessment is a key stage to …
expectancy in their highest grade. Therefore, treatment assessment is a key stage to …
HDC-Net: Hierarchical decoupled convolution network for brain tumor segmentation
Accurate segmentation of brain tumor from magnetic resonance images (MRIs) is crucial for
clinical treatment decision and surgical planning. Due to the large diversity of the tumors and …
clinical treatment decision and surgical planning. Due to the large diversity of the tumors and …