Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks

AbstractDeep learning is nowadays at the forefront of artificial intelligence. More precisely, the use of convolutional neural networks has drastically improved the learning capabilities of computer vision applications, being able to directly consider raw data without any prior feature extraction. Advanced methods in the machine learning field, such as adaptive momentum algorithms or dropout regularization, have dramatically improved the convolutional neural networks predicting ability, outperforming that of conventional fully connected neural networks. This work summarizes, in an intended didactic way, the main aspects of these cutting-edge techniques from a medical imaging perspective.

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Train Deep Learning Models using subsurface geological images datasets

10.5194/egusphere-egu21-6385 ◽

2021 ◽

Author(s):

Ramy Abdallah ◽

Clare E. Bond ◽

Robert W.H. Butler

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Learning Models ◽

Structural Interpretation ◽

Performance Accuracy ◽

Learning Techniques ◽

Machine Learning Models

<p>Machine learning is being presented as a new solution for a wide range of geoscience problems. Primarily machine learning has been used for 3D seismic data processing, seismic facies analysis and well log data correlation. The rapid development in technology with open-source artificial intelligence libraries and the accessibility of affordable computer graphics processing units (GPU) makes the application of machine learning in geosciences increasingly tractable. However, the application of artificial intelligence in structural interpretation workflows of subsurface datasets is still ambiguous. This study aims to use machine learning techniques to classify images of folds and fold-thrust structures. Here we show that convolutional neural networks (CNNs) as supervised deep learning techniques provide excellent algorithms to discriminate between geological image datasets. Four different datasets of images have been used to train and test the machine learning models. These four datasets are a seismic character dataset with five classes (faults, folds, salt, flat layers and basement), folds types with three classes (buckle, chevron and conjugate), fault types with three classes (normal, reverse and thrust) and fold-thrust geometries with three classes (fault bend fold, fault propagation fold and detachment fold). These image datasets are used to investigate three machine learning models. One Feedforward linear neural network model and two convolutional neural networks models (Convolution 2d layer transforms sequential model and Residual block model (ResNet with 9, 34, and 50 layers)). Validation and testing datasets forms a critical part of testing the model&#8217;s performance accuracy. The ResNet model records the highest performance accuracy score, of the machine learning models tested. Our CNN image classification model analysis provides a framework for applying machine learning to increase structural interpretation efficiency, and shows that CNN classification models can be applied effectively to geoscience problems. The study provides a starting point to apply unsupervised machine learning approaches to sub-surface structural interpretation workflows.</p>

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An artificial intelligence algorithm that differentiates anterior ethmoidal artery location on sinus computed tomography scans

The Journal of Laryngology & Otology ◽

10.1017/s0022215119002536 ◽

2019 ◽

Vol 134 (1) ◽

pp. 52-55 ◽

Cited By ~ 3

Author(s):

J Huang ◽

A-R Habib ◽

D Mendis ◽

J Chong ◽

M Smith ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Computed Tomography ◽

Neural Networks ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Sinus Surgery ◽

Computed Tomography Scans ◽

Ethmoidal Artery ◽

Anterior Ethmoidal Artery

AbstractObjectiveDeep learning using convolutional neural networks represents a form of artificial intelligence where computers recognise patterns and make predictions based upon provided datasets. This study aimed to determine if a convolutional neural network could be trained to differentiate the location of the anterior ethmoidal artery as either adhered to the skull base or within a bone ‘mesentery’ on sinus computed tomography scans.MethodsCoronal sinus computed tomography scans were reviewed by two otolaryngology residents for anterior ethmoidal artery location and used as data for the Google Inception-V3 convolutional neural network base. The classification layer of Inception-V3 was retrained in Python (programming language software) using a transfer learning method to interpret the computed tomography images.ResultsA total of 675 images from 388 patients were used to train the convolutional neural network. A further 197 unique images were used to test the algorithm; this yielded a total accuracy of 82.7 per cent (95 per cent confidence interval = 77.7–87.8), kappa statistic of 0.62 and area under the curve of 0.86.ConclusionConvolutional neural networks demonstrate promise in identifying clinically important structures in functional endoscopic sinus surgery, such as anterior ethmoidal artery location on pre-operative sinus computed tomography.

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Artificial intelligence‐based detection of pharyngeal cancer using convolutional neural networks

Digestive Endoscopy ◽

10.1111/den.13653 ◽

2020 ◽

Vol 32 (7) ◽

pp. 1057-1065 ◽

Cited By ~ 6

Author(s):

Atsuko Tamashiro ◽

Toshiyuki Yoshio ◽

Akiyoshi Ishiyama ◽

Tomohiro Tsuchida ◽

Kazunori Hijikata ◽

...

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Pharyngeal Cancer

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Realtime Emotional Reflective User Interface Based on Deep Convolutional Neural Networks and Generative Adversarial Networks

Electronics ◽

10.3390/electronics11010118 ◽

2021 ◽

Vol 11 (1) ◽

pp. 118

Author(s):

Holly Burrows ◽

Javad Zarrin ◽

Lakshmi Babu-Saheer ◽

Mahdi Maktab-Dar-Oghaz

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

User Interface ◽

Convolutional Neural Networks ◽

Mental Health Problems ◽

Well Being ◽

Working Environment ◽

Generative Adversarial Networks ◽

Deep Convolutional Neural Networks ◽

Adversarial Networks

It is becoming increasingly apparent that a significant amount of the population suffers from mental health problems, such as stress, depression, and anxiety. These issues are a result of a vast range of factors, such as genetic conditions, social circumstances, and lifestyle influences. A key cause, or contributor, for many people is their work; poor mental state can be exacerbated by jobs and a person’s working environment. Additionally, as the information age continues to burgeon, people are increasingly sedentary in their working lives, spending more of their days seated, and less time moving around. It is a well-known fact that a decrease in physical activity is detrimental to mental well-being. Therefore, the need for innovative research and development to combat negativity early is required. Implementing solutions using Artificial Intelligence has great potential in this field of research. This work proposes a solution to this problem domain, utilising two concepts of Artificial Intelligence, namely, Convolutional Neural Networks and Generative Adversarial Networks. A CNN is trained to accurately predict when an individual is experiencing negative emotions, achieving a top accuracy of 80.38% with a loss of 0.42. A GAN is trained to synthesise images from an input domain that can be attributed to evoking position emotions. A Graphical User Interface is created to display the generated media to users in order to boost mood and reduce feelings of stress. The work demonstrates the capability for using Deep Learning to identify stress and negative mood, and the strategies that can be implemented to reduce them.

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Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks

Clinical Radiology ◽

10.1016/j.crad.2017.11.015 ◽

2018 ◽

Vol 73 (5) ◽

pp. 439-445 ◽

Cited By ~ 82

Author(s):

D.H. Kim ◽

T. MacKinnon

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Fracture Detection ◽

Deep Convolutional Neural Networks

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Deep learning in Emergency Medicine: Recent contributions and methodological challenges

Emergency Care Journal ◽

10.4081/ecj.2020.8573 ◽

2020 ◽

Vol 16 (1) ◽

Cited By ~ 1

Author(s):

Francesco Faita

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Emergency Medicine ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Recurrent Neural Networks ◽

Computational Models ◽

Clinical Usefulness ◽

Human Knowledge ◽

Deep Convolutional Neural Networks

In the last few years, artificial intelligence (AI) technology has grown dramatically impacting several fields of human knowledge and medicine in particular. Among other approaches, deep learning, which is a subset of AI based on specific computational models, such as deep convolutional neural networks and recurrent neural networks, has shown exceptional performance in images and signals processing. Accordingly, emergency medicine will benefit from the adoption of this technology. However, a particular attention should be devoted to the review of these papers in order to exclude overoptimistic results from clinically transferable ones. We presented a group of studies recently published on PubMed and selected by keywords ‘deep learning emergency medicine’ and ‘artificial intelligence emergency medicine’ with the aim of highlighting their methodological strengths and weaknesses, as well as their clinical usefulness.

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An Approximate GEMM Unit for Energy-Efficient Object Detection

Sensors ◽

10.3390/s21124195 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4195

Author(s):

Ratko Pilipović ◽

Vladimir Risojević ◽

Janko Božič ◽

Patricio Bulić ◽

Uroš Lotrič

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Neural Networks ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Energy Efficient ◽

Graphics Processing Units ◽

Matrix Multiplication ◽

Approximate Computing ◽

General Matrix

Edge computing brings artificial intelligence algorithms and graphics processing units closer to data sources, making autonomy and energy-efficient processing vital for their design. Approximate computing has emerged as a popular strategy for energy-efficient circuit design, where the challenge is to achieve the best tradeoff between design efficiency and accuracy. The essential operation in artificial intelligence algorithms is the general matrix multiplication (GEMM) operation comprised of matrix multiplication and accumulation. This paper presents an approximate general matrix multiplication (AGEMM) unit that employs approximate multipliers to perform matrix–matrix operations on four-by-four matrices given in sixteen-bit signed fixed-point format. The synthesis of the proposed AGEMM unit to the 45 nm Nangate Open Cell Library revealed that it consumed only up to 36% of the area and 25% of the energy required by the exact general matrix multiplication unit. The AGEMM unit is ideally suited to convolutional neural networks, which can adapt to the error induced in the computation. We evaluated the AGEMM units’ usability for honeybee detection with the YOLOv4-tiny convolutional neural network. The results implied that we can deploy the AGEMM units in convolutional neural networks without noticeable performance degradation. Moreover, the AGEMM unit’s employment can lead to more area- and energy-efficient convolutional neural network processing, which in turn could prolong sensors’ and edge nodes’ autonomy.

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