Overview of deep learning in medical imaging

By Kenji Suzuki. 2017

1,082 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, background): “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, 1”
Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images (2018, background): “114 At the present, researchers across the world have begun to apply DL tools and obtain promising 115 results in a wide variety of medical image analyses/understanding tasks (Rajaraman et al., 2017; 116 Suzuki, 2017).”
IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge. (2020, methodology): “Hence, it is emerging as an effective tool for machine learning, promising to reshape the future of automated medical image analysis ( Greenspan et al., 2016; Litjens et al., 2017; Suzuki, 2017; Shen et al., 2017; Kim et al., 2018; Ker et al., 2018 ).”
Deep Learning and Medical Diagnosis: A Review of Literature (2018, positive): “One of the main applications of deep learning is for medical diagnosis [9,10].”
Deep Learning on 1-D Biosignals: a Taxonomy-based Survey (2018, methodology): “In particular, convolutional neural networks (CNNs) have been designed for solving complex image analysis tasks [15, 17].”
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging (2020, background): “Deep learning is the core technology of the rising artificial intelligence and has reported significantly diagnostic accuracy in medical imaging for automatic detection of lung diseases [59], [60], [61] .”
Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey (2020, positive): “DL methods have been used excessively in medical image processing and related studies (Lundervold & Lundervold, 2019; Suzuki, 2017).”
Evaluating Bayesian Deep Learning Methods for Semantic Segmentation (2018, background): “With the increasing use of AI systems in real-life scenarios like autonomous driving [42, 31, 7, 40, 58] and medical diagnosis [17, 61, 56, 45], there are many cases in which additional knowledge about the model’s conﬁdence, i.e. capturing whether the model is essentially ‘guessing at random’,…”
MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques (2023, methodology): “6, 7 and 8, the proposed neural networks showed significant improvement over previous ones in detecting brain MRI image features and classifying them into three types of tumors and one class of healthy brain.”