Deep into the Brain: Artificial Intelligence in Stroke Imaging

Lee, Eun‐Jae; Kim, Yonghwan; Kim, Namkug; Kang, Dong‐Wha

doi:10.5853/jos.2017.02054

Cited by 196 publications

(134 citation statements)

References 49 publications

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“…There are several thorough reviews and overviews of the field to consult for more information, across modalities and organs, and with different points of view and level of technical details. For example the comprehensive review [102] 27 , covering both medicine and biology and spanning from imaging applications in healthcare to protein-protein interaction and uncertainty quantification; key concepts of deep learning for clinical radiologists [103,104,105,106,107,108,109,110,111,112], including radiomics and imaging genomics (radiogenomics) [113], and toolkits and libraries for deep learning [114]; deep learning in neuroimaging and neuroradiology [115]; brain segmentation [116]; stroke imaging [117,118]; neuropsychiatric disorders [119]; breast cancer [120,121]; chest imaging [122]; imaging in oncology [123,124,125]; medical ultrasound [126,127]; and more technical surveys of deep learning in medical image analysis [42,128,129,130]. Finally, for those who like to be hands-on, there are many instructive introductory deep learning tutorials available online.…”

Section: Deep Learning Medical Imaging and Mrimentioning

confidence: 99%

An overview of deep learning in medical imaging focusing on MRI

Lundervold

2019

Zeitschrift für Medizinische Physik

1,430

883

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What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI.Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging. Artificial neural networksArtificial neural networks (ANNs) is one of the most famous machine learning models, introduced already in the 1950s, and actively studied since [41, Chapter 1.2]. 11 Roughly, a neural network consists of a number of connected computational units, called neurons, arranged in layers. There's an input layer where data enters the network, followed by one or more hidden layers transforming the data as it flows through, before ending at an output layer that produces the neural network's predictions. The network is trained to output useful predictions by identifying patterns in a set of labeled training data, fed through the network while the outputs are compared with the actual labels by an objective function. During training the network's parametersthe strength of each neuron-is tuned until the patterns identified by the network result in good 10 https://www.deeplearningbook.org/ 11 The loose connection between artificial neural networks and neural networks in the brain is often mentioned, but quite over-blown considering the complexity of biological neural networks. However, there is some interesting recent work connecting neuroscience and artificial neural networks, indicating an increase in the cross-fertilization between the two fields [43,44,45].3 predictions for the training data. Once the patterns are learned, the network can be used to make predictions on new, unseen data, i.e. generalize to new data.It h...

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Section: Deep Learning Medical Imaging and Mrimentioning

confidence: 99%

An overview of deep learning in medical imaging focusing on MRI

Lundervold

2019

Zeitschrift für Medizinische Physik

1,430

883

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“…In computer science, DCNNs have become state of the art for interpreting images and are the model of choice for the annual ImageNet Large Scale Visual Recognition Competition . AI has already shown potential across healthcare with examples including dermatology for skin lesion identification, ophthalmology for the detection of diabetic retinopathy, orthopaedics for hand and ankle fractures and in radiology for interpreting chest X‐rays for tuberculosis and interpreting CT and MRI for detecting strokes to name a few.…”

Section: Introductionmentioning

confidence: 99%

Computer vs human: Deep learning versus perceptual training for the detection of neck of femur fractures

et al. 2018

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Introduction: To evaluate the accuracy of deep convolutional neural networks (DCNNs) for detecting neck of femur (NoF) fractures on radiographs, in comparison with perceptual training in medically-na€ ıve individuals. Methods: This study extends a previous study that conducted perceptual training in medically-na€ ıve individuals for the detection of NoF fractures on a variety of dataset sizes. The same anteroposterior hip radiograph dataset was used to train two DCNNs (AlexNet and GoogLeNet) to detect NoF fractures. For direct comparison with perceptual training results, deep learning was completed across a variety of dataset sizes (200, 320 and 640 images) with images split into training (80%) and validation (20%). An additional 160 images were used as the final test set. Multiple pre-processing and augmentation techniques were utilised. Results: AlexNet and GoogLeNet DCNNs NoF fracture detection accuracy increased with larger training dataset sizes and mildly with augmentation. Accuracy increased from 81.9% and 88.1% to 89.4% and 94.4% for AlexNet and GoogLeNet respectively. Similarly, the test accuracy for the perceptual training in top-performing medically-na€ ıve individuals increased from 87.6% to 90.5% when trained on 640 images compared with 200 images. Conclusions: Single detection tasks in radiology are commonly used in DCNN research with their results often used to make broader claims about machine learning being able to perform as well as subspecialty radiologists. This study suggests that as impressive as recognising fractures is for a DCNN, similar learning can be achieved by top-performing medically-na€ ıve humans with less than 1 hour of perceptual training.

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“…In [238] the authors argue that the continued success of this field depends on sustained technological advancements in information technology and computer architecture as well as collaboration and open exchange of data between physicians and other stakeholders. Lee et al [250] conclude that international cooperation is required for constructing a high quality multimodal big dataset for stroke imaging. Another solution to better exploit big medical data in cardiology is to apply unsupervised learning methods, which do not require annotations.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

Deep Learning in Cardiology

Bizopoulos

Koutsouris

2019

IEEE Rev. Biomed. Eng.

130

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The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.

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Deep into the Brain: Artificial Intelligence in Stroke Imaging

Cited by 196 publications

References 49 publications

An overview of deep learning in medical imaging focusing on MRI

An overview of deep learning in medical imaging focusing on MRI

Computer vs human: Deep learning versus perceptual training for the detection of neck of femur fractures

Deep Learning in Cardiology

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