Journal ArticleDOI
Deep Learning in Medical Image Analysis
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The article was published on 2017-10-18. It has received 243 citations till now. The article focuses on the topics: Deep learning.read more
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Journal ArticleDOI
Artificial intelligence in radiology
Ahmed Hosny,Chintan Parmar,John Quackenbush,Lawrence H. Schwartz,Lawrence H. Schwartz,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts +6 more
TL;DR: A general understanding of AI methods, particularly those pertaining to image-based tasks, is established and how these methods could impact multiple facets of radiology is explored, with a general focus on applications in oncology.
Journal ArticleDOI
Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis
TL;DR: In this paper, the authors survey the current research on applying deep learning to clinical tasks based on EHR data, where they find a variety of deep learning techniques and frameworks being applied to several types of clinical applications including information extraction, representation learning, outcome prediction, phenotyping, and deidentification.
Journal ArticleDOI
Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success
TL;DR: Artificial intelligence offers a new and promising set of methods for analyzing image data, and radiologists will explore these new pathways and are likely to play a leading role in medical applications of AI.
Journal ArticleDOI
Identification of plant leaf diseases using a nine-layer deep convolutional neural network
G. Geetharamani,J Arun Pandian +1 more
TL;DR: It is observed that using data augmentation can increase the performance of the model, and the proposed model achieves better performance when using the validation data.
Journal ArticleDOI
Artificial intelligence and digital pathology: Challenges and opportunities
TL;DR: This paper strives to provide a realistic account of all challenges and opportunities of adopting AI algorithms in digital pathology from both engineering and pathology perspectives.
References
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Journal ArticleDOI
Artificial intelligence in radiology
Ahmed Hosny,Chintan Parmar,John Quackenbush,Lawrence H. Schwartz,Lawrence H. Schwartz,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts +6 more
TL;DR: A general understanding of AI methods, particularly those pertaining to image-based tasks, is established and how these methods could impact multiple facets of radiology is explored, with a general focus on applications in oncology.
Journal ArticleDOI
Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success
TL;DR: Artificial intelligence offers a new and promising set of methods for analyzing image data, and radiologists will explore these new pathways and are likely to play a leading role in medical applications of AI.
Journal ArticleDOI
Deep semantic segmentation of natural and medical images: a review
TL;DR: This review categorizes the leading deep learning-based medical and non-medical image segmentation solutions into six main groups of deep architectural, data synthesis- based, loss function-based, sequenced models, weakly supervised, and multi-task methods.
Journal ArticleDOI
Identification of plant leaf diseases using a nine-layer deep convolutional neural network
G. Geetharamani,J Arun Pandian +1 more
TL;DR: It is observed that using data augmentation can increase the performance of the model, and the proposed model achieves better performance when using the validation data.
Journal ArticleDOI
Artificial intelligence and digital pathology: Challenges and opportunities
TL;DR: This paper strives to provide a realistic account of all challenges and opportunities of adopting AI algorithms in digital pathology from both engineering and pathology perspectives.