MRI-Based Deep Learning Analysis Can Predict Microsatellite Instability in Rectal Cancer

2020 ◽  
Author(s):  
Wei Zhang ◽  
Zixing Huang ◽  
Jian Zhao ◽  
Du He ◽  
Mou Li ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Deep Learning ◽  
Microsatellite Instability ◽  
Learning Analysis

PO-1718: Deep learning based auto-contouring of planning CT scans for rectal cancer

2020 ◽  
Vol 152 ◽  
pp. S949
Author(s):  
L. Bokhorst ◽  
M.H.F. Savenije ◽  
M.P.W. Intven ◽  
C.A.T. Van den Berg
Keyword(s):  
Rectal Cancer ◽  
Deep Learning ◽  
Ct Scans ◽  
Planning Ct

An Effective Approach for Malware Detection and Explanation via Deep Learning Analysis

2021 ◽  
Author(s):  
Huozhu Wang ◽  
Ziyuan Zhu ◽  
Zhongkai Tong ◽  
Xiang Yin ◽  
Yusi Feng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Malware Detection ◽  
Learning Analysis

Making Data Big for a Deep-learning Analysis: Aggregation of Public COVID-19 Datasets of Lung Computed Tomography Scans

2021 ◽  
Author(s):  
Francesca Lizzi ◽  
Francesca Brero ◽  
Raffaella Cabini ◽  
Maria Fantacci ◽  
Stefano Piffer ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Computed Tomography Scans ◽  
Learning Analysis

scholarly journals General recipe to form input space for deep learning analysis of HEP scattering processes

2020 ◽  
Vol 35 (21) ◽  
pp. 2050119
Author(s):  
Lev Dudko ◽  
Georgi Vorotnikov ◽  
Petr Volkov ◽  
Maxim Perfilov ◽  
Andrei Chernoded ◽  
...  
Keyword(s):  
Deep Learning ◽  
Multivariate Data Analysis ◽  
Hard Scattering ◽  
Input Space ◽  
Low Level ◽  
Multivariate Technique ◽  
Deep Learning Neural Network ◽  
Learning Analysis ◽  
Sophisticated Analysis ◽  
Kinematic Properties

Deep learning neural network (DNN) technique is one of the most efficient and general approach of multivariate data analysis of the collider experiments. The important step of the analysis is the optimization of the input space for multivariate technique. In the paper we propose the general recipe how to form the set of low-level observables sensitive to the differences in hard scattering processes at the colliders. It is shown in the paper that without any sophisticated analysis of the kinematic properties one can achieve close to optimal performance of DNN with the proposed general set of low-level observables.

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