Development and Validation of a Deep Neural Network for Accurate Evaluation of Endoscopic Images From Patients With Ulcerative Colitis

2020 ◽  
Vol 158 (8) ◽  
pp. 2150-2157 ◽  
Author(s):  
Kento Takenaka ◽  
Kazuo Ohtsuka ◽  
Toshimitsu Fujii ◽  
Mariko Negi ◽  
Kohei Suzuki ◽  
...  
Keyword(s):  
Neural Network ◽  
Ulcerative Colitis ◽  
Deep Neural Network ◽  
Accurate Evaluation ◽  
Endoscopic Images ◽  
Development And Validation

Deep neural network accurately predicts prognosis of ulcerative colitis using endoscopic images

2021 ◽  
Author(s):  
Kento Takenaka ◽  
Kazuo Ohtsuka ◽  
Toshimitsu Fujii ◽  
Shiori Oshima ◽  
Ryuichi Okamoto ◽  
...  
Keyword(s):  
Neural Network ◽  
Ulcerative Colitis ◽  
Deep Neural Network ◽  
Endoscopic Images

Deep neural network approaches for detecting gastric polyps in endoscopic images

2021 ◽  
Author(s):  
Serdar Durak ◽  
Bülent Bayram ◽  
Tolga Bakırman ◽  
Murat Erkut ◽  
Metehan Doğan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Gastric Polyps ◽  
Endoscopic Images ◽  
Network Approaches

scholarly journals 3D Deep Neural Network Segmentation of Intracerebral Hemorrhage: Development and Validation for Clinical Trials

2020 ◽  
Author(s):  
Matthew F. Sharrock ◽  
W. Andrew Mould ◽  
Hasan Ali ◽  
Meghan Hildreth ◽  
Issam A. Awad ◽  
...  
Keyword(s):  
Neural Network ◽  
Clinical Trials ◽  
Intracerebral Hemorrhage ◽  
Deep Neural Network ◽  
Development And Validation

scholarly journals Predicting quantum potentials by deep neural network and metropolis sampling

2021 ◽  
Vol 4 (3) ◽  
Author(s):  
Rui Hong ◽  
Peng-Fei Zhou ◽  
Bin Xi ◽  
Jie Hu ◽  
An-Chun Ji ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Schrödinger Equation ◽  
Schrodinger Equation ◽  
Quantum Physics ◽  
Deep Neural Network ◽  
Accurate Evaluation ◽  
Ab Initio Simulations ◽  
Accuracy And Stability ◽  
Excellent Accuracy

The hybridizations of machine learning and quantum physics have caused essential impacts to the methodology in both fields. Inspired by quantum potential neural network, we here propose to solve the potential in the Schrödinger equation provided the eigenstate, by combining Metropolis sampling with deep neural network, which we dub as Metropolis potential neural network (MPNN). A loss function is proposed to explicitly involve the energy in the optimization for its accurate evaluation. Benchmarking on the harmonic oscillator and hydrogen atom, MPNN shows excellent accuracy and stability on predicting not just the potential to satisfy the Schrödinger equation, but also the eigen-energy. Our proposal could be potentially applied to the ab-initio simulations, and to inversely solving other partial differential equations in physics and beyond.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification
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