scholarly journals AUTOMATIC ANALYSIS OF MICROSCOPY IMAGES USING THE DLGRAM01 CLOUD SERVICE

2021 ◽  
pp. 300-311
Author(s):  
Андрей Викторович Матвеев ◽  
Михаил Юрьевич Машуков ◽  
Анна Владимировна Нартова ◽  
Наталья Николаевна Санькова ◽  
Алексей Григорьевич Окунев
Keyword(s):  
Neural Network ◽  
Materials Science ◽  
Cloud Service ◽  
Statistical Parameters ◽  
The Neural Network ◽  
Quantitative Image ◽  
Subjective Factor ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Automated Image Processing

Исследование материалов методами микроскопии нередко включает стадию подсчета количества наблюдаемых объектов и определения их статистических параметров, для чего необходимо измерять сотни объектов. В работе описан облачный сервис DLgram01, который позволяет специалистам в области материаловедения, не имеющих навыков программирования, выполнять автоматизированную обработку изображений - определять количество и параметры (площадь, размер) изучаемых объектов. Сервис разработан с использованием новейших достижений в области глубокого машинного обучения, для обучения нейронной сети пользователю необходимо разметить несколько изучаемых объектов. Обучение нейронной сети производится автоматически за несколько минут. Важными особенностями сервиса DLgram01 является возможность корректировать результаты предсказания нейронной сети, а также получение детальной информации о всех распознанных объектах. Использование сервиса позволяет существенно сократить временные затраты на количественный анализ изображений, снизить влияние субъективного фактора, повысить точность анализа и его эргоемкость. The study of materials by microscopy often includes counting the number of observed objects and determining their statistical parameters, for which it is necessary to measure hundreds of objects. The created DLgram01 cloud service allows specialists in the field of materials science who do not have programming skills to perform automated image processing - to determine the number and parameters (area, size) of the objects under study. The service is developed using the latest achievements in the field of deep machine learning. To train a neural network, the user needs to label only several objects. The neural network is trained automatically in a few minutes. Important features of the DLgram01 service are the ability to adjust the results of neural network prediction, as well as obtaining detailed information about all recognized objects. Using the service allows to significantly decrease the time for quantitative image analysis, reduce the influence of the subjective factor, increase the accuracy of the analysis and its ergo-intensity.

scholarly journals Axle Temperature Monitoring and Neural Network Prediction Analysis for High-Speed Train under Operation

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1662
Author(s):  
Wei Hao ◽  
Feng Liu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
High Speed ◽  
Prediction Method ◽  
High Speed Train ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Trained Neural Network

Predicting the axle temperature states of the high-speed train under operation in advance and evaluating working states of axle bearings is important for improving the safety of train operation and reducing accident risks. The method of monitoring the axle temperature of a train under operation, combined with the neural network prediction method, was applied. A total of 36 sensors were arranged at key positions such as the axle bearings of the train gearbox and the driving end of the traction motor. The positions of the sensors were symmetrical. Axle temperature measurements over 11 days with more than 38,000 km were obtained. The law of the change of the axle temperature in each section was obtained in different environments. The resultant data from the previous 10 days were used to train the neural network model, and a total of 800 samples were randomly selected from eight typical locations for the prediction of axle temperature over the following 3 min. In addition, the results predicted by the neural network method and the GM (1,1) method were compared. The results show that the predicted temperature of the trained neural network model is in good agreement with the experimental temperature, with higher precision than that of the GM (1,1) method, indicating that the proposed method is sufficiently accurate and can be a reliable tool for predicting axle temperature.

scholarly journals Artificial intelligence for advance requesting of immunohistochemistry in diagnostically uncertain prostate biopsies

2021 ◽  
Author(s):  
Andrea Chatrian ◽  
Richard T. Colling ◽  
Lisa Browning ◽  
Nasullah Khalid Alham ◽  
Korsuk Sirinukunwattana ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Area Under The Curve ◽  
Validation Dataset ◽  
Prostate Biopsies ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Neural Network Algorithm ◽  
Network Prediction ◽  
Whole Slide Images

AbstractThe use of immunohistochemistry in the reporting of prostate biopsies is an important adjunct when the diagnosis is not definite on haematoxylin and eosin (H&E) morphology alone. The process is however inherently inefficient with delays while waiting for pathologist review to make the request and duplicated effort reviewing a case more than once. In this study, we aimed to capture the workflow implications of immunohistochemistry requests and demonstrate a novel artificial intelligence tool to identify cases in which immunohistochemistry (IHC) is required and generate an automated request. We conducted audits of the workflow for prostate biopsies in order to understand the potential implications of automated immunohistochemistry requesting and collected prospective cases to train a deep neural network algorithm to detect tissue regions that presented ambiguous morphology on whole slide images. These ambiguous foci were selected on the basis of the pathologist requesting immunohistochemistry to aid diagnosis. A gradient boosted trees classifier was then used to make a slide-level prediction based on the outputs of the neural network prediction. The algorithm was trained on annotations of 219 immunohistochemistry-requested and 80 control images, and tested by threefold cross-validation. Validation was conducted on a separate validation dataset of 222 images. Non IHC-requested cases were diagnosed in 17.9 min on average, while IHC-requested cases took 33.4 min over multiple reporting sessions. We estimated 11 min could be saved on average per case by automated IHC requesting, by removing duplication of effort. The tool attained 99% accuracy and 0.99 Area Under the Curve (AUC) on the test data. In the validation, the average agreement with pathologists was 0.81, with a mean AUC of 0.80. We demonstrate the proof-of-principle that an AI tool making automated immunohistochemistry requests could create a significantly leaner workflow and result in pathologist time savings.

scholarly journals Artificial Intelligence for Advance Requesting of Immunohistochemistry in Diagnostically Uncertain Prostate Biopsies

2021 ◽  
Author(s):  
Andrea Chatrian ◽  
Richard Colling ◽  
Lisa Browning ◽  
Nasullah Khalid Alham ◽  
Korsuk Sirinukunwattana ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Area Under The Curve ◽  
Validation Dataset ◽  
Prostate Biopsies ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Neural Network Algorithm ◽  
Network Prediction ◽  
Whole Slide Images

The use of immunohistochemistry in the reporting of prostate biopsies is an important adjunct when the diagnosis is not definite on haematoxylin and eosin (H&E) morphology alone. The process is however inherently inefficient with delays while waiting for pathologist review to make the request and duplicated effort reviewing a case more than once. In this study, we aimed to capture the workflow implications of immunohistochemistry requests and demonstrate a novel artificial intelligence tool to identify cases in which immunohistochemistry (IHC) is required and generate an automated request. We conducted audits of the workflow for prostate biopsies in order to understand the potential implications of automated immunohistochemistry requesting and collected prospective cases to train a deep neural network algorithm to detect tissue regions that presented ambiguous morphology on whole slide images. These ambiguous foci were selected on the basis of the pathologist requesting immunohistochemistry to aid diagnosis. A gradient boosted trees classifier was then used to make a slide level prediction based on the outputs of the neural network prediction. The algorithm was trained on annotations of 219 immunohistochemistry-requested and 80 control images, and tested by 3-fold cross-validation. Validation was conducted on a separate validation dataset of 212 images. Non IHC-requested cases were diagnosed in 17.9 minutes on average, while IHC-requested cases took 33.4 minutes over multiple reporting sessions. We estimated 11 minutes could be saved on average per case by automated IHC requesting, by removing duplication of effort. The tool attained 99% accuracy and 0.99 Area Under the Curve (AUC) on the test data. In the validation, the average agreement with pathologists was 0.81, with a mean AUC of 0.80. We demonstrate the proof-of-principle that an AI tool making automated immunohistochemistry requests could create a significantly leaner workflow and result in pathologist time savings.

Neural network prediction of K and L-shell X-ray production cross sections

2018 ◽  
Vol 106 (12) ◽  
pp. 1017-1021
Author(s):  
Brahim Beladel ◽  
Brahim Mohamedi ◽  
Abdelkader Guesmia ◽  
Mohamed E. A. Benamar
Keyword(s):  
Neural Network ◽  
Cross Section ◽  
Cross Sections ◽  
Production Cross ◽  
Alpha Particles ◽  
X Ray ◽  
Fundamental Parameters ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Network Prediction

Abstract The ionization and X-ray production cross section are fundamental parameters in elemental analysis by PIXE technique. Unfortunately no exact general analytical expression exists, from which the interest of this work. In this paper, we apply the neural network technique in the evaluation of the X-ray production cross sections. The calculations are based on Mukoyama’s PWBA data. Our results are compared with experimental data for protons and alpha particles for energies ranging from hundreds KeV to tens MeV.

Research and Development of LM Neural Network Prediction System for Grind-Hardening

2009 ◽  
Vol 416 ◽  
pp. 248-252 ◽  
Author(s):  
Zhong Feng Pan ◽  
Gui Cheng Wang ◽  
Chong Lue Hua ◽  
Hong Jie Pei
Keyword(s):  
Neural Network ◽  
Convergence Rate ◽  
Bp Neural Network ◽  
Prediction System ◽  
Slow Convergence ◽  
Hardening Process ◽  
The Neural Network ◽  
Grind Hardening ◽  
Neural Network Prediction ◽  
Network Prediction

An improved neural network based on L-M algorithm has been applied to the prediction of the grind-hardening parameters against to the slow convergence rate of conventional BP neural network. And the the neural network model for grind-hardening is established. The neural network prediction system for grind-hardening process has been developed based on L-M algorithm. The functions of system is analyzed, particularly and some pivotal technology to realize the system are put forward.

Input Parameters Determination for Predicting Ram Speed and Billet Temperature for the First Billet

2008 ◽  
Vol 367 ◽  
pp. 161-168 ◽  
Author(s):  
Marc Sabater ◽  
Maria Luisa García-Romeu ◽  
Joaquim de Ciurana
Keyword(s):  
Neural Network ◽  
Preliminary Analysis ◽  
Extrusion Process ◽  
Tool Development ◽  
Extrusion Speed ◽  
Billet Temperature ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Ram Speed

The aim of this paper is to present the results of the first step of a defined methodology for the neural network tool development. That first step is to studying the variables that have influence on extrusion process, especially in those that affect billet temperature and extrusion speed. In order to determine those parameters, a preliminary analysis was conducted with experimental data from real industry. Then, a multiple regression analysis was carried out to define which parameters will be the inputs of the neural network prediction tool.

Two Types of Neural Network Prediction Models and Time Complexity

2014 ◽  
Vol 548-549 ◽  
pp. 985-989
Author(s):  
Ji Min Zhang ◽  
Liang Zhu ◽  
Subhash Rekheja
Keyword(s):  
Neural Network ◽  
Time Complexity ◽  
Prediction Models ◽  
Lateral Acceleration ◽  
Neural Network Models ◽  
Pass Filter ◽  
Low Pass Filter ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Network Prediction

The linear adaptive neural network and RBF neural network, according to the measured low-pass filter lateral acceleration signal, was used to establish the reference lateral acceleration applied for the input of tilting train control system. This paper presents the two types of neural network models and prediction algorithms, and studies the time complexity of the two types of network algorithms. The results show that time complexity of the neural network prediction is closely related to its parameters, the neural network structure also can lead to the difference in their calculation time, and RBF prediction neural network spends the minimum time.

Sign in / Sign up

Export Citation Format

Share Document