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Estimating Uncertainty of Geographic Atrophy Segmentations with Bayesian Deep Learning [PDF]

Ophthalmology Science
Purpose: To apply methods for quantifying uncertainty of deep learning segmentation of geographic atrophy (GA). Design: Retrospective analysis of OCT images and model comparison.
Theodore Spaide, PhD, Anand E. Rajesh, MD, Nayoon Gim, Marian Blazes, MD, Cecilia S. Lee, MD, MS, Niranchana Macivannan, PhD, Gary Lee, PhD, MEng, Warren Lewis, MS, Ali Salehi, PhD, Luis de Sisternes, PhD, Gissel Herrera, MD, Mengxi Shen, MD, PhD, Giovanni Gregori, PhD, Philip J. Rosenfeld, MD, PhD, Varsha Pramil, MD, MS, Nadia Waheed, MD, MPH, Yue Wu, PhD, Qinqin Zhang, PhD, Aaron Y. Lee, MD, MSCI   +18 more
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Combining K-fold cross validation with bayesian hyperparameter optimization for accuracy enhancement of land cover and land use classification [PDF]

Scientific Reports
Land cover and land use (LCLU) information is crucial in different earth observation applications, such as environmental management, infrastructure planning, and urban development.
Pooya Heidari, Asghar Milan
doaj   +2 more sources

A Review on Bayesian Deep Learning in Healthcare: Applications and Challenges

IEEE Access, 2022
In the last decade, Deep Learning (DL) has revolutionized the use of artificial intelligence, and it has been deployed in different fields of healthcare applications such as image processing, natural language processing, and signal processing.
Abdullah A. Abdullah, Masoud M. Hassan, Yaseen T. Mustafa   +2 more
doaj   +1 more source

Optimizing Automated Trading Systems with Deep Reinforcement Learning

Algorithms, 2023
In this paper, we propose a novel approach to optimize parameters for strategies in automated trading systems. Based on the framework of Reinforcement learning, our work includes the development of a learning environment, state representation, reward ...
Minh Tran, Duc Pham-Hi, Marc Bui
doaj   +1 more source

Deep learning does not replace Bayesian modeling: Comparing research use via citation counting

Applied AI Letters, 2022
One could be excused for assuming that deep learning had or will soon usurp all credible work in reasoning, artificial intelligence, and statistics, but like most “meme” class broad generalizations the concept does not hold up to scrutiny.
Breck Baldwin
doaj   +1 more source

Bayesian Neural Networks for Reversible Steganography

IEEE Access, 2022
Recent advances in deep learning have led to a paradigm shift in the field of reversible steganography. A fundamental pillar of reversible steganography is predictive modelling which can be realised via deep neural networks.
Ching-Chun Chang
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Revisiting Bayesian Autoencoders With MCMC

IEEE Access, 2022
Autoencoders gained popularity in the deep learning revolution given their ability to compress data and provide dimensionality reduction. Although prominent deep learning methods have been used to enhance autoencoders, the need to provide robust ...
Rohitash Chandra, Mahir Jain, Manavendra Maharana, Pavel N. Krivitsky   +3 more
doaj   +1 more source

Prediction for Manufacturing Factors in a Steel Plate Rolling Smart Factory Using Data Clustering-Based Machine Learning

IEEE Access, 2020
A Steel Plate Rolling Mill (SPM) is a milling machine that uses rollers to press hot slab inputs to produce ferrous or non-ferrous metal plates. To produce high-quality steel plates, it is important to precisely detect and sense values of manufacturing ...
Cheol Young Park, Jin Woog Kim, Bosung Kim, Joongyoon Lee   +3 more
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Deep Bayesian Gaussian processes for uncertainty estimation in electronic health records

Scientific Reports, 2021
One major impediment to the wider use of deep learning for clinical decision making is the difficulty of assigning a level of confidence to model predictions.
Yikuan Li, Shishir Rao, Abdelaali Hassaine, Rema Ramakrishnan, Dexter Canoy, Gholamreza Salimi-Khorshidi, Mohammad Mamouei, Thomas Lukasiewicz, Kazem Rahimi   +8 more
doaj   +1 more source

Bayesian Graph Convolutional Neural Networks via Tempered MCMC

IEEE Access, 2021
Deep learning models, such as convolutional neural networks, have long been applied to image and multi-media tasks, particularly those with structured data.
Rohitash Chandra, Ayush Bhagat, Manavendra Maharana, Pavel N. Krivitsky   +3 more
doaj   +1 more source