Rapid and accurate intraoperative pathological diagnosis by artificial intelligence with deep learning technology

2017 ◽  
Vol 107 ◽  
pp. 98-99 ◽  
Author(s):  
Jing Zhang ◽  
Yanlin Song ◽  
Fan Xia ◽  
Chenjing Zhu ◽  
Yingying Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Pathological Diagnosis ◽  
Learning Technology

scholarly journals Using CycleGAN to Achieve the Sketch Recognition Process of Sketch-Based Modeling

2021 ◽  
pp. 26-34
Author(s):  
Yuqian Li ◽  
Weiguo Xu
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Learning Technology ◽  
Modeling Technology ◽  
Image Translation ◽  
Mapping Process ◽  
And Training ◽  
Difficult Part ◽  
2D Sketches

AbstractArchitects usually design ideation and conception by hand-sketching. Sketching is a direct expression of the architect’s creativity. But 2D sketches are often vague, intentional and even ambiguous. In the research of sketch-based modeling, it is the most difficult part to make the computer to recognize the sketches. Because of the development of artificial intelligence, especially deep learning technology, Convolutional Neural Networks (CNNs) have shown obvious advantages in the field of extracting features and matching, and Generative Adversarial Neural Networks (GANs) have made great breakthroughs in the field of architectural generation which make the image-to-image translation become more and more popular. As the building images are gradually developed from the original sketches, in this research, we try to develop a system from the sketches to the images of buildings using CycleGAN algorithm. The experiment demonstrates that this method could achieve the mapping process from the sketches to images, and the results show that the sketches’ features could be recognised in the process. By the learning and training process of the sketches’ reconstruction, the features of the images are also mapped to the sketches, which strengthen the architectural relationship in the sketch, so that the original sketch can gradually approach the building images, and then it is possible to achieve the sketch-based modeling technology.

Deep learning in bioengineering and biofabrication: a powerful technology boosting translation from research to clinics

2021 ◽  
Author(s):  
João B Costa ◽  
Joana Silva-Correia ◽  
Rui L Reis ◽  
Joaquim M Oliveira
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Cost Effectiveness ◽  
Clinical Setting ◽  
Emerging Technologies ◽  
Cutting Edge ◽  
Learning Technology ◽  
3D Bioprinting ◽  
Massive Datasets

Bioengineering has been revolutionizing the production of biofunctional tissues for tackling unmet clinical needs. Bioengineers have been focusing their research in biofabrication, especially 3D bioprinting, providing cutting-edge approaches and biomimetic solutions with more reliability and cost–effectiveness. However, these emerging technologies are still far from the clinical setting and deep learning, as a subset of artificial intelligence, can be widely explored to close this gap. Thus, deep-learning technology is capable to autonomously deal with massive datasets and produce valuable outputs. The application of deep learning in bioengineering and how the synergy of this technology with biofabrication can help (more efficiently) bring 3D bioprinting to clinics, are overviewed herein.

scholarly journals An On-Device Deep Learning Approach to Battery Saving on Industrial Mobile Terminals

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4044
Author(s):  
Inyeop Choi ◽  
Hyogon Kim
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Energy Consumption ◽  
Energy Use ◽  
Freezing Point ◽  
Learning Technology ◽  
Mobile Terminals ◽  
Logistics Industry ◽  
Varying Environments ◽  
Current Dissipation

The mobile terminals used in the logistics industry can be exposed to wildly varying environments, which may hinder effective operation. In particular, those used in cold storages can be subject to frosting in the scanner window when they are carried out of the warehouses to a room-temperature space outside. To prevent this, they usually employ a film heater on the scanner window. However, the temperature and humidity conditions of the surrounding environment and the temperature of the terminal itself that cause frosting vary widely. Due to the complicated frost-forming conditions, existing industrial mobile terminals choose to implement rather simple rules that operate the film heater well above the freezing point, which inevitably leads to inefficient energy use. This paper demonstrates that to avoid such waste, on-device artificial intelligence (AI) a.k.a. edge AI can be readily employed to industrial mobile terminals and can improve their energy efficiency. We propose an artificial-intelligence-based approach that utilizes deep learning technology to avoid the energy-wasting defrosting operations. By combining the traditional temperature-sensing logic with a convolutional neural network (CNN) classifier that visually checks for frost, we can more precisely control the defrosting operation. We embed the CNN classifier in the device and demonstrate that the approach significantly reduces the energy consumption. On our test terminal, the net ratio of the energy consumption by the existing system to that of the edge AI for the heating film is almost 14:1. Even with the common current-dissipation accounted for, our edge AI system would increase the operating hours by 86%, or by more than 6 h compared with the system without the edge AI.

Self-organizing cognitive model synthesis with deep learning support

2018 ◽  
Vol 7 (2.28) ◽  
pp. 168 ◽  
Author(s):  
A Raikov ◽  
A Ermakov ◽  
A Merkulov
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Cognitive Model ◽  
Complete Information ◽  
Big Data Analysis ◽  
Cognitive Models ◽  
Learning Technology ◽  
Problem Situation ◽  
Learning Support ◽  
Model Synthesis

Cognitive models are created by experts and the process takes a lot of time. Furthermore, the result of expert work needs to be verified especially in cases when experts do not have complete information and cannot understand the problem situation quickly. As was previously shown cognitive models’ factors and their mutual relationships could be verified with applying Big Data analysis technology. This paper addresses the issue of automated cognitive models synthesis on the base of author’s convergent methodology, artificial intelligence and deep learning technology. 

scholarly journals Design of College Evaluation System Based on Artificial Intelligence and Deep Learning Technology

CONVERTER ◽  
2021 ◽  
pp. 651-658
Author(s):  
Jiang Yan, Wang Peipei
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Performance Appraisal ◽  
Evaluation System ◽  
Learning Technology ◽  
Functional Requirements ◽  
Appraisal System ◽  
Performance Appraisal System ◽  
Artificial Intelligence Technology ◽  
Business Requirements

Artificial intelligence and deep learning technology are important technologies widely used in manufacturing industry.With the help of performance appraisal system to comprehensively evaluate the performance of teachers is a good measure. Therefore, it is very necessary to develop a performance appraisal system for university teachers by using artificial intelligence technology. This paper first demonstrates the feasibility of the development of performance appraisal system, and scientifically divides the user roles. According to the business requirements, the core business process of the system is established, and the system architecture and functional modules are designed. At the same time, this paper establishes the conceptual model and logical model of database. Finally, SSH framework and extjs framework are used to realize the functions of the system. In this paper, the reliability, stability and security of the system are tested to ensure that the system meets the functional and non functional requirements. The operation results show that the system has stable functions, simple operation and convenient maintenance, and basically meets the needs of users at different levels.

scholarly journals Design of Fault Diagnosis Algorithm for Electromechanical System Based on Artificial Intelligence

2021 ◽  
Vol 2050 (1) ◽  
pp. 012011
Author(s):  
Fuyou Zhao ◽  
Mingying Huo ◽  
Naiming Qi ◽  
Lianfeng Li ◽  
Weiwei Cui
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Data Sets ◽  
Learning Technology ◽  
Electrical System ◽  
Fault Recognition ◽  
Fault Features ◽  
Diagnosis Method

Abstract A relatively perfect system for the fault diagnosis of mechanical and electrical products has been formed through decades of development. Nevertheless, the traditional fault diagnosis methods fail to cope with the gradual huge mechanical and electrical system. As a result, the advantages of fault diagnosis mode driven by data are increasingly prominent. Meanwhile, the effect of fault diagnosis has exceeded the traditional fault diagnosis methods in many fields. Through the use of the deep learning technology based on artificial intelligence, it carries out mapping and fitting. By fully taking advantages of neural network, it can effectively obtain the accurate classification of fault data. A fault diagnosis method based on the fault data of mechanical and electrical system is designed in this thesis. When it comes to the basic process, it is to take data sets for different mechanical and electrical products. Through the use of feature engineering method, it extracts the fault features of data. Through the use of deep learning technology, it carries out the intelligent diagnosis. According to the experimental results, it indicates that the fault diagnosis method based on deep learning technology can distinguish a variety of fault modes in mechanical and electrical system in an effective way. What’s more, good classification results in fault recognition have been achieved by a variety of deep convolutional neural network structures, so the feasibility of the method is further verified.

APPLICATION OF DEEP LEARNING IN MEDICAL IMAGING

2020 ◽  
Vol 03 (04) ◽  
pp. 7-13
Author(s):  
Elcin Nizami Huseyn ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Medical Imaging ◽  
Key Words ◽  
Learning Technology ◽  
Application Research ◽  
Imaging Diagnosis ◽  
Experience Machine

Medical imaging technology plays an important role in the detection, diagnosis and treatment of diseases. Due to the instability of human expert experience, machine learning technology is expected to assist researchers and physicians to improve the accuracy of imaging diagnosis and reduce the imbalance of medical resources. This article systematically summarizes some methods of deep learning technology, introduces the application research of deep learning technology in medical imaging, and discusses the limitations of deep learning technology in medical imaging. Key words: Artificial Intelligence, Deep Learning, Medical Imaging, big data

Implementation and Practice of Deep Learning-Based Instance Segmentation Algorithm for Quantification of Hepatic Fibrosis at Whole Slide Level in Sprague-Dawley Rats

2021 ◽  
pp. 019262332110571
Author(s):  
Ji-Hee Hwang ◽  
Hyun-Ji Kim ◽  
Heejin Park ◽  
Byoung-Seok Lee ◽  
Hwa-Young Son ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Hepatic Fibrosis ◽  
Ground Truth ◽  
Learning Technology ◽  
Sprague Dawley ◽  
Sprague Dawley Rats ◽  
Deep Learning Model ◽  
Instance Segmentation ◽  
Whole Slide Images

Exponential development in artificial intelligence or deep learning technology has resulted in more trials to systematically determine the pathological diagnoses using whole slide images (WSIs) in clinical and nonclinical studies. In this study, we applied Mask Regions with Convolution Neural Network (Mask R-CNN), a deep learning model that uses instance segmentation, to detect hepatic fibrosis induced by N-nitrosodimethylamine (NDMA) in Sprague-Dawley rats. From 51 WSIs, we collected 2011 cropped images with hepatic fibrosis annotations. Training and detection of hepatic fibrosis via artificial intelligence methods was performed using Tensorflow 2.1.0, powered by an NVIDIA 2080 Ti GPU. From the test process using tile images, 95% of model accuracy was verified. In addition, we validated the model to determine whether the predictions by the trained model can reflect the scoring system by the pathologists at the WSI level. The validation was conducted by comparing the model predictions in 18 WSIs at 20× and 10× magnifications with ground truth annotations and board-certified pathologists. Predictions at 20× showed a high correlation with ground truth ( R 2 = 0.9660) and a good correlation with the average fibrosis rank by pathologists ( R 2 = 0.8887). Therefore, the Mask R-CNN algorithm is a useful tool for detecting and quantifying pathological findings in nonclinical studies.

scholarly journals Analysis of Physical Expansion Training Based on Edge Computing and Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhongle Liu
Keyword(s):  
Artificial Intelligence ◽  
Resource Allocation ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Rapid Development ◽  
Computing Time ◽  
Edge Computing ◽  
Learning Technology ◽  
Effective Development ◽  
Physical Expansion

The effective development of physical expansion training benefits from the rapid development of computer technology, especially the integration of Edge Computing (EC) and Artificial Intelligence (AI) technology. Physical expansion training is mainly based on the collective form, and how to improve the quality of training to achieve results has become the content of everyone’s attention. As a representative technology in the field of AI, deep learning and EC evolving from traditional cloud computing technology are all well applied to physical expansion training. Traditional EC methods have problems such as high computing cost and long computing time. In this paper, deep learning technology is introduced to optimize EC methods. The EC cycle is set through the Internet of Things (IoT) topology to obtain the data upload speed. The CNN (Convolutional Neural Network) model introduces deep reinforcement learning technology, implements convolution calculations, and completes the resource allocation of EC for each trainer’s wearable sensor device, which realizes the optimization of EC based on deep reinforcement learning. The experiment results show that the proposed method can effectively control the server’s occupancy time, the energy cost of the edge server, and the computing cost. The proposed method in this paper can also improve the resource allocation ability of EC, ensure the uniform speed of the computing process, and improve the efficiency of EC.

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