[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 …
Artificial intelligence in ultrasound
YT Shen, L Chen, WW Yue, HX Xu - European Journal of Radiology, 2021 - Elsevier
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Elsevier logo Journals & Books Search RegisterSign in View PDF Download full issue …
Elsevier logo Journals & Books Search RegisterSign in View PDF Download full issue …
Autonomous environment-adaptive microrobot swarm navigation enabled by deep learning-based real-time distribution planning
Navigating a large swarm of micro-/nanorobots is critical for potential targeted
delivery/therapy applications owing to the limited volume/function of a single microrobot, and …
delivery/therapy applications owing to the limited volume/function of a single microrobot, and …
Confidence calibration and predictive uncertainty estimation for deep medical image segmentation
Fully convolutional neural networks (FCNs), and in particular U-Nets, have achieved state-of-
the-art results in semantic segmentation for numerous medical imaging applications …
the-art results in semantic segmentation for numerous medical imaging applications …
AI-based automatic detection and classification of diabetic retinopathy using U-Net and deep learning
A Bilal, L Zhu, A Deng, H Lu, N Wu - Symmetry, 2022 - mdpi.com
Artificial intelligence is widely applied to automate Diabetic retinopathy diagnosis. Diabetes-
related retinal vascular disease is one of the world's most common leading causes of …
related retinal vascular disease is one of the world's most common leading causes of …
Transfer learning in medical image segmentation: New insights from analysis of the dynamics of model parameters and learned representations
D Karimi, SK Warfield, A Gholipour - Artificial intelligence in medicine, 2021 - Elsevier
We present a critical assessment of the role of transfer learning in training fully convolutional
networks (FCNs) for medical image segmentation. We first show that although transfer …
networks (FCNs) for medical image segmentation. We first show that although transfer …
Convolution-free medical image segmentation using transformers
D Karimi, SD Vasylechko, A Gholipour - … 1, 2021, proceedings, part I 24, 2021 - Springer
Like other applications in computer vision, medical image segmentation and his email
address have been most successfully addressed using deep learning models that rely on …
address have been most successfully addressed using deep learning models that rely on …
[HTML][HTML] Predictive uncertainty estimation for out-of-distribution detection in digital pathology
Abstract Machine learning model deployment in clinical practice demands real-time risk
assessment to identify situations in which the model is uncertain. Once deployed, models …
assessment to identify situations in which the model is uncertain. Once deployed, models …
A Transfer Learning and U-Net-based automatic detection of diabetic retinopathy from fundus images
Diabetic retinopathy (DR) is an ocular manifestation of diabetes and the leading cause of
visual impairment and blindness across the globe. Early detection and treatment of DR can …
visual impairment and blindness across the globe. Early detection and treatment of DR can …
A novel approach for diabetic retinopathy screening using asymmetric deep learning features
Automatic screening of diabetic retinopathy (DR) is a well-identified area of research in the
domain of computer vision. It is challenging due to structural complexity and a marginal …
domain of computer vision. It is challenging due to structural complexity and a marginal …