[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 …
Automatic road extraction from remote sensing imagery using ensemble learning and postprocessing
High-resolution satellite images contain valuable road semantic information, but the
occlusion of vegetation and buildings and the sparse distribution and heterogeneous …
occlusion of vegetation and buildings and the sparse distribution and heterogeneous …
Pixel-wise gradient uncertainty for convolutional neural networks applied to out-of-distribution segmentation
In recent years, deep neural networks have defined the state-of-the-art in semantic
segmentation where their predictions are constrained to a predefined set of semantic …
segmentation where their predictions are constrained to a predefined set of semantic …
Improving video instance segmentation by light-weight temporal uncertainty estimates
Instance segmentation with neural networks is an essential task in environment perception.
In many works, it has been observed that neural networks can predict false positive …
In many works, it has been observed that neural networks can predict false positive …
Leveraging voxel-wise segmentation uncertainty to improve reliability in assessment of paediatric dysplasia of the hip
Purpose Estimating uncertainty in predictions made by neural networks is critically important
for increasing the trust medical experts have in automatic data analysis results. In …
for increasing the trust medical experts have in automatic data analysis results. In …
Multi-Scale Foreground-Background Confidence for Out-of-Distribution Segmentation
S Marschall, K Maag - arxiv preprint arxiv:2412.16990, 2024 - arxiv.org
Deep neural networks have shown outstanding performance in computer vision tasks such
as semantic segmentation and have defined the state-of-the-art. However, these …
as semantic segmentation and have defined the state-of-the-art. However, these …
Uncertainty-based assessment of hip joint segmentation and 3D ultrasound scan adequacy in paediatric dysplasia measurement using deep learning
A Kannan - 2022 - open.library.ubc.ca
Abstract Developmental Dysplasia of the Hip (DDH)-a condition characterized by hip joint
instability, is one of the most common hip disorders in newborns. Clinical practice for …
instability, is one of the most common hip disorders in newborns. Clinical practice for …
[PDF][PDF] Estimating uncertainty of deep learning-based segmentation for prostate cancer radiotherapy
M Leousi - cig-utrecht.org
In radiotherapy, structure delineation is crucial to ensure accurate irradiation of the target
sparing the adjacent organs. Recently, deep learning-based segmentations achieved …
sparing the adjacent organs. Recently, deep learning-based segmentations achieved …