A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Moloud Abdar,Farhad Pourpanah,Sadiq Hussain,Dana Rezazadegan,Li Liu,Mohammad Ghavamzadeh,Paul Fieguth,Xiaochun Cao,Abbas Khosravi,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Vladimir Makarenkov,Saeid Nahavandi +13 more
TLDR
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of uncertainties during both optimization and decision making processes as mentioned in this paper, and have been applied to solve a variety of real-world problems in science and engineering Bayesian approximation and ensemble learning techniques are two widely-used types of uncertainty quantification.About:
This article is published in Information Fusion.The article was published on 2021-12-01 and is currently open access. It has received 77 citations till now. The article focuses on the topics: Ensemble learning & Uncertainty quantification.read more
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Recent advances and clinical applications of deep learning in medical image analysis.
Xuxin Chen,Ximin Wang,Ke Zhang,Roy Zhang,Kar Ming Fung,Theresa C. Thai,Kathleen N. Moore,Robert S. Mannel,Hong Liu,Bin Zheng,Yuchen Qiu +10 more
TL;DR: A comprehensive overview of applying deep learning methods in various medical image analysis tasks can be found in this article, where the authors highlight the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical images, which are summarized based on different application scenarios.
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Recent Advances and Applications of Deep Learning Methods in Materials Science
Kamal Choudhary,Kamal Choudhary,Brian L. DeCost,Chi Chen,Anubhav Jain,Francesca Tavazza,Ryan Cohn,Cheol WooPark,Alok Choudhary,Ankit Agrawal,Simon J. L. Billinge,Elizabeth A. Holm,Shyue Ping Ong,Chris Wolverton +13 more
TL;DR: Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities as mentioned in this paper.
Journal ArticleDOI
BARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification
Moloud Abdar,Mohammad Amin Fahami,Satarupa Chakrabarti,Abbas Khosravi,Paweł Pławiak,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Ryszard Tadeusiewicz,Saeid Nahavandi +9 more
TL;DR: A novel, simple and effective fusion model with uncertainty-aware module for medical image classification called Binary Residual Feature fusion (BARF) is proposed and the Monte Carlo dropout during inference is applied to obtain the mean and standard deviation of the predictions.
Journal ArticleDOI
Deep learning based synthetic-CT generation in radiotherapy and PET: A review
TL;DR: A systematic review of deep learning-based methods for the generation of synthetic computed tomography (sCT) is presented in this article, where the authors classify the methods into three categories according to their clinical applications: (i) to replace computed tomograms in magnetic resonance (MR) based treatment planning, (ii) facilitate cone-beam computed tomograph based image-guided adaptive radiotherapy, and (iii) derive attenuation maps for the correction of positron emission tomography.
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Machine learning for hydrologic sciences: An introductory overview
Tianfang Xu,Feng Liang +1 more
TL;DR: An overview of machine learning in hydrologic sciences provides a non‐technical introduction, placed within a historical context, to commonly used machine learning algorithms and deep learning architectures.
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