scholarly journals Deep learning-enabled medical computer vision

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andre Esteva ◽  
Katherine Chou ◽  
Serena Yeung ◽  
Nikhil Naik ◽  
Ali Madani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Medical Imaging ◽  
Real World ◽  
Recent Progress ◽  
Medical Applications ◽  
Modern Computer ◽  
Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

scholarly journals Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies

BMJ ◽  
2020 ◽  
pp. m689 ◽  
Author(s):  
Myura Nagendran ◽  
Yang Chen ◽  
Christopher A Lovejoy ◽  
Anthony C Gordon ◽  
Matthieu Komorowski ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Clinical Trials ◽  
Deep Learning ◽  
Medical Imaging ◽  
Prediction Model ◽  
Real World ◽  
Risk Of Bias ◽  
Reporting Standards ◽  
Randomised Trials

Abstract Objective To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians. Design Systematic review. Data sources Medline, Embase, Cochrane Central Register of Controlled Trials, and the World Health Organization trial registry from 2010 to June 2019. Eligibility criteria for selecting studies Randomised trial registrations and non-randomised studies comparing the performance of a deep learning algorithm in medical imaging with a contemporary group of one or more expert clinicians. Medical imaging has seen a growing interest in deep learning research. The main distinguishing feature of convolutional neural networks (CNNs) in deep learning is that when CNNs are fed with raw data, they develop their own representations needed for pattern recognition. The algorithm learns for itself the features of an image that are important for classification rather than being told by humans which features to use. The selected studies aimed to use medical imaging for predicting absolute risk of existing disease or classification into diagnostic groups (eg, disease or non-disease). For example, raw chest radiographs tagged with a label such as pneumothorax or no pneumothorax and the CNN learning which pixel patterns suggest pneumothorax. Review methods Adherence to reporting standards was assessed by using CONSORT (consolidated standards of reporting trials) for randomised studies and TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) for non-randomised studies. Risk of bias was assessed by using the Cochrane risk of bias tool for randomised studies and PROBAST (prediction model risk of bias assessment tool) for non-randomised studies. Results Only 10 records were found for deep learning randomised clinical trials, two of which have been published (with low risk of bias, except for lack of blinding, and high adherence to reporting standards) and eight are ongoing. Of 81 non-randomised clinical trials identified, only nine were prospective and just six were tested in a real world clinical setting. The median number of experts in the comparator group was only four (interquartile range 2-9). Full access to all datasets and code was severely limited (unavailable in 95% and 93% of studies, respectively). The overall risk of bias was high in 58 of 81 studies and adherence to reporting standards was suboptimal (<50% adherence for 12 of 29 TRIPOD items). 61 of 81 studies stated in their abstract that performance of artificial intelligence was at least comparable to (or better than) that of clinicians. Only 31 of 81 studies (38%) stated that further prospective studies or trials were required. Conclusions Few prospective deep learning studies and randomised trials exist in medical imaging. Most non-randomised trials are not prospective, are at high risk of bias, and deviate from existing reporting standards. Data and code availability are lacking in most studies, and human comparator groups are often small. Future studies should diminish risk of bias, enhance real world clinical relevance, improve reporting and transparency, and appropriately temper conclusions. Study registration PROSPERO CRD42019123605.

scholarly journals Application of Artificial Intelligence in Medical Imaging Diagnosis

2020 ◽  
Vol 9 (1) ◽  
pp. 5
Author(s):  
Linyi Zhang
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Image Segmentation ◽  
Deep Learning ◽  
Medical Imaging ◽  
Accurate Diagnosis ◽  
Data Sets ◽  
Special Role ◽  
Imaging Diagnosis ◽  
Learning Data

Both the treatment of cancer and other serious diseases often depends on the diagnosis of artificial complexity and heavy experience. The introduction of artificial intelligence in medical imaging has injected vitality into the diagnosis of images. Artificial intelligence uses deep learning, image segmentation, neural networks and other algorithms flexibly in image recognition through learning data sets to extract features for accurate diagnosis of clinical diseases. At the same time, it also plays a special role in controlling the spread of infectious diseases such as new coronary pneumonia.

Image Classification Based On CNN: A Survey

2021 ◽  
pp. PP. 18-50
Author(s):  
Ahmed A. Elngar ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Vision System ◽  
Machine Learning Algorithms ◽  
Traffic Lights

Computer vision is one of the fields of computer science that is one of the most powerful and persuasive types of artificial intelligence. It is similar to the human vision system, as it enables computers to recognize and process objects in pictures and videos in the same way as humans do. Computer vision technology has rapidly evolved in many fields and contributed to solving many problems, as computer vision contributed to self-driving cars, and cars were able to understand their surroundings. The cameras record video from different angles around the car, then a computer vision system gets images from the video, and then processes the images in real-time to find roadside ends, detect other cars, and read traffic lights, pedestrians, and objects. Computer vision also contributed to facial recognition; this technology enables computers to match images of people’s faces to their identities. which these algorithms detect facial features in images and then compare them with databases. Computer vision also play important role in Healthcare, in which algorithms can help automate tasks such as detecting Breast cancer, finding symptoms in x-ray, cancerous moles in skin images, and MRI scans. Computer vision also contributed to many fields such as image classification, object discovery, motion recognition, subject tracking, and medicine. The rapid development of artificial intelligence is making machine learning more important in his field of research. Use algorithms to find out every bit of data and predict the outcome. This has become an important key to unlocking the door to AI. If we had looked to deep learning concept, we find deep learning is a subset of machine learning, algorithms inspired by structure and function of the human brain called artificial neural networks, learn from large amounts of data. Deep learning algorithm perform a task repeatedly, each time tweak it a little to improve the outcome. So, the development of computer vision was due to deep learning. Now we'll take a tour around the convolution neural networks, let us say that convolutional neural networks are one of the most powerful supervised deep learning models (abbreviated as CNN or ConvNet). This name ;convolutional ; is a token from a mathematical linear operation between matrixes called convolution. CNN structure can be used in a variety of real-world problems including, computer vision, image recognition, natural language processing (NLP), anomaly detection, video analysis, drug discovery, recommender systems, health risk assessment, and time-series forecasting. If we look at convolutional neural networks, we see that CNN are similar to normal neural networks, the only difference between CNN and ANN is that CNNs are used in the field of pattern recognition within images mainly. This allows us to encode the features of an image into the structure, making the network more suitable for image-focused tasks, with reducing the parameters required to set-up the model. One of the advantages of CNN that it has an excellent performance in machine learning problems. So, we will use CNN as a classifier for image classification. So, the objective of this paper is that we will talk in detail about image classification in the following sections.

scholarly journals Artificial Intelligence and Deep Learning for Weapon Identification in Security Systems

2021 ◽  
Vol 2089 (1) ◽  
pp. 012079
Author(s):  
Makkena Brahmaiah ◽  
Srinivasa Rao Madala ◽  
Ch Mastan Chowdary
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Deep Learning ◽  
Video Surveillance ◽  
Real World ◽  
Crime Rates ◽  
Surveillance Systems ◽  
Security Systems ◽  
Trade Off ◽  
Wide Range

Abstract As crime rates rise at large events and possibly lonely places, security is always a top concern in every field. A wide range of issues may be solved with the use of computer vision, including anomalous detection and monitoring. Intelligence monitoring is becoming more dependent on video surveillance systems that can recognise and analyse scene and anomaly occurrences. Using SSD and Faster RCNN techniques, this paper provides automated gun (or weapon) identification. Use of two different kinds of datasets is included in the proposed approach. As opposed to the first dataset, the second one comprises pictures that have been manually tagged. However, the trade-off between speed and precision in real-world situations determines whether or not each method will be useful.

scholarly journals Artificial Intelligence & Machine Learning in Computer Vision Applications

2020 ◽  
Vol 7 (1) ◽  
pp. 2-3
Author(s):  
Shadi Saleh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Large Scale ◽  
Short Term Memory ◽  
Graphics Processing Unit ◽  
Multimedia Data ◽  
Processing Unit

Deep learning and machine learning innovations are at the core of the ongoing revolution in Artificial Intelligence for the interpretation and analysis of multimedia data. The convergence of large-scale datasets and more affordable Graphics Processing Unit (GPU) hardware has enabled the development of neural networks for data analysis problems that were previously handled by traditional handcrafted features. Several deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTM)/Gated Recurrent Unit (GRU), Deep Believe Networks (DBN), and Deep Stacking Networks (DSNs) have been used with new open source software and libraries options to shape an entirely new scenario in computer vision processing.

Counterfactual Autoencoder for Unsupervised Semantic Learning

2020 ◽  
pp. 720-736
Author(s):  
Saad Sadiq ◽  
Mei-Ling Shyu ◽  
Daniel J. Feaster
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Language Processing ◽  
Speech Processing ◽  
Semantic Learning ◽  
Novel Approach ◽  
Training Samples ◽  
Latent Space

Deep Neural Networks (DNNs) are best known for being the state-of-the-art in artificial intelligence (AI) applications including natural language processing (NLP), speech processing, computer vision, etc. In spite of all recent achievements of deep learning, it has yet to achieve semantic learning required to reason about the data. This lack of reasoning is partially imputed to the boorish memorization of patterns and curves from millions of training samples and ignoring the spatiotemporal relationships. The proposed framework puts forward a novel approach based on variational autoencoders (VAEs) by using the potential outcomes model and developing the counterfactual autoencoders. The proposed framework transforms any sort of multimedia input distributions to a meaningful latent space while giving more control over how the latent space is created. This allows us to model data that is better suited to answer inference-based queries, which is very valuable in reasoning-based AI applications.

scholarly journals Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars

2020 ◽  
Vol 17 (168) ◽  
pp. 20200446 ◽  
Author(s):  
Blanca Jiménez-García ◽  
José Aznarte ◽  
Natalia Abellán ◽  
Enrique Baquedano ◽  
Manuel Domínguez-Rodrigo
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Surface Modifications ◽  
High Accuracy ◽  
Dental Morphology ◽  
Bone Surface ◽  
Bone Accumulation ◽  
Tooth Marks

Taphonomists have long struggled with identifying carnivore agency in bone accumulation and modification. Now that several taphonomic techniques allow identifying carnivore modification of bones, a next step involves determining carnivore type. This is of utmost importance to determine which carnivores were preying on and competing with hominins and what types of interaction existed among them during prehistory. Computer vision techniques using deep architectures of convolutional neural networks (CNN) have enabled significantly higher resolution in the identification of bone surface modifications (BSM) than previous methods. Here, we apply these techniques to test the hypothesis that different carnivores create specific BSM that can enable their identification. To make differentiation more challenging, we selected two types of carnivores (lions and jaguars) that belong to the same mammal family and have similar dental morphology. We hypothesize that if two similar carnivores can be identified by the BSM they imprint on bones, then two more distinctive carnivores (e.g. hyenids and felids) should be more easily distinguished. The CNN method used here shows that tooth scores from both types of felids can be successfully classified with an accuracy greater than 82%. The first hypothesis was successfully tested. The next step will be to differentiate diverse carnivore types involving a wider range of carnivore-made BSM. The present study demonstrates that resolution increases when combining two different disciplines (taphonomy and artificial intelligence computing) in order to test new hypotheses that could not be addressed with traditional taphonomic methods.

scholarly journals COMPUTER VISION FOR HUMAN-MACHINE INTERACTION-REVIEW

2019 ◽  
Vol 2019 (02) ◽  
pp. 131-139 ◽  
Author(s):  
Dr. Suma V.
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Human Vision ◽  
Human Machine Interaction ◽  
The Neural Networks ◽  
Security Surveillance ◽  
Heath Care ◽  
Machine Interaction

The paper is a review on the computer vision that is helpful in the interaction between the human and the machines. The computer vision that is termed as the subfield of the artificial intelligence and the machine learning is capable of training the computer to visualize, interpret and respond back to the visual world in a similar way as the human vision does. Nowadays the computer vision has found its application in broader areas such as the heath care, safety security, surveillance etc. due to the progress, developments and latest innovations in the artificial intelligence, deep learning and neural networks. The paper presents the enhanced capabilities of the computer vision experienced in various applications related to the interactions between the human and machines involving the artificial intelligence, deep learning and the neural networks.

scholarly journals Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Malte Seemann ◽  
Lennart Bargsten ◽  
Alexander Schlaefer
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Deep Learning ◽  
Medical Imaging ◽  
Computed Tomography Angiography ◽  
Data Augmentation ◽  
Domain Adaptation ◽  
Synthetic Image ◽  
Wide Range ◽  
The Impact

AbstractDeep learning methods produce promising results when applied to a wide range of medical imaging tasks, including segmentation of artery lumen in computed tomography angiography (CTA) data. However, to perform sufficiently, neural networks have to be trained on large amounts of high quality annotated data. In the realm of medical imaging, annotations are not only quite scarce but also often not entirely reliable. To tackle both challenges, we developed a two-step approach for generating realistic synthetic CTA data for the purpose of data augmentation. In the first step moderately realistic images are generated in a purely numerical fashion. In the second step these images are improved by applying neural domain adaptation. We evaluated the impact of synthetic data on lumen segmentation via convolutional neural networks (CNNs) by comparing resulting performances. Improvements of up to 5% in terms of Dice coefficient and 20% for Hausdorff distance represent a proof of concept that the proposed augmentation procedure can be used to enhance deep learning-based segmentation for artery lumen in CTA images.

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