Rule-based Computer Aided Decision Making for Traumatic Brain Injuries

2013 ◽  
pp. 229-259 ◽  
Author(s):  
Ashwin Belle ◽  
Soo-Yeon Ji ◽  
Wenan Chen ◽  
Toan Huynh ◽  
Kayvan Najarian
Keyword(s):  
Decision Making ◽  
Brain Injuries ◽  
Traumatic Brain Injuries ◽  
Rule Based ◽  
Computer Aided ◽  
Computer Aided Decision

Computer-Aided Decision-Making System for Endometrial Atypical Hyperplasia based on Multimodal & Multi-Instance Deep Convolution Networks

2021 ◽  
Author(s):  
Xin Liao ◽  
Qingli Li ◽  
Xin Zheng ◽  
Jin He
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Atypical Hyperplasia ◽  
Diagnosis And Treatment ◽  
Small Scale ◽  
Prognostic Analysis ◽  
Computer Aided ◽  
Decision Making System ◽  
Suspected Lesion ◽  
Computer Aided Decision

Abstract The pathological diagnosis is the gold standard for neoplasms and their precursors, which is highly relevant to the treatment planning and the prognostic analysis. Currently, deep learning networks have been used for the pathological computer-assisted diagnosis and treatment decision-makings. However, due to extremely large size of the whole slide images (WSIs) of pathological slides, the prevailing deep learning models are un-applicable directly in the WSIs analysis. Moreover, the precise exclusion of the blank regions and interfere regions, as well as the manual annotation of various lesioned and normal regions in super large WSIs are infeasible and unavailable in clinical practice. To address aforementioned problems, we develop an computer-aided decision-making system based on multimodal and multi-instance deep convolution networks (CNN) to assist in the diagnosis and treatment of endometrial atypical hyperplasia (AH)/ endometrial intraepithelial hyperplasia (EIH). Firstly, we set up the frame-work of computer-aided decision-making system based on the WSIs image patterns of AH/EIH, and then transfer the large-scale WSI analysis to the small-scale analysis of multiple suspected lesion regions which can be accomplished the major computer vision models, and eventually the results of prognostic analysis for multiple small-scale suspected lesion regions are summarized to obtain the prognostic results of WSIs by the decision supporting algorithm based on the cognition intelligence. We validate the method via experimental analysis of 102 endometrial atypical hyperplasia patients at the West China Second University Hospital of Sichuan University. The performance achieved for endometrial AH/EIH prognostic analysis includes accuracy (85.3%), precision (84.6%), recall (86.3%). Meanwhile, the method has superior performance to prognostic judgment of a single pathologist as well as approximates to analysis results determined by three pathologists according to the majority voting method.

A chemometric tool for computer-aided decision making in analytical laboratory management

1987 ◽  
Vol 6 (10) ◽  
pp. 252-259 ◽  
Author(s):  
B.G.M. Vandeginste ◽  
J.W.A. Klaessens ◽  
G. Kateman
Keyword(s):  
Decision Making ◽  
Analytical Laboratory ◽  
Laboratory Management ◽  
Computer Aided ◽  
Computer Aided Decision

Computer Aided Decision Making in Water Retention and Allocation

1987 ◽  
Vol 20 (9) ◽  
pp. 761-766
Author(s):  
D.A. Donciulescu ◽  
F.G. Filip ◽  
Th.D. Popescu
Keyword(s):  
Decision Making ◽  
Water Retention ◽  
Computer Aided ◽  
Computer Aided Decision

Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm

2017 ◽  
Vol 141 ◽  
pp. 19-26 ◽  
Author(s):  
Zeinab Arabasadi ◽  
Roohallah Alizadehsani ◽  
Mohamad Roshanzamir ◽  
Hossein Moosaei ◽  
Ali Asghar Yarifard
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Decision Making ◽  
Heart Disease ◽  
Disease Detection ◽  
Hybrid Neural Network ◽  
Computer Aided ◽  
Computer Aided Decision

scholarly journals Computer-aided decision-making for predicting liver disease using PSO-based optimized SVM with feature selection

2019 ◽  
Vol 17 ◽  
pp. 100255 ◽  
Author(s):  
Javad Hassannataj Joloudari ◽  
Hamid Saadatfar ◽  
Abdollah Dehzangi ◽  
Shahaboddin Shamshirband
Keyword(s):  
Decision Making ◽  
Feature Selection ◽  
Liver Disease ◽  
Computer Aided ◽  
Computer Aided Decision

Points: Computer aided decision making

BMJ ◽  
1984 ◽  
Vol 289 (6452) ◽  
pp. 1148-1148
Author(s):  
V. Coleman
Keyword(s):  
Decision Making ◽  
Computer Aided ◽  
Computer Aided Decision
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