Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique

By Hayit Greenspan, B. van Ginneken, Ronald M. Summers. 2016

842 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

Improving Interpretability of Deep Neural Networks with Semantic Information (2017, background): “With the aids of these interpretable features, human users can easily visualize and interact with the system, which allows a human-in-the-loop learning procedure. age analysis [15], ﬁnancial investment [1], etc .”
Generalisation in humans and deep neural networks (2018, background): “1 Secondly, features learned on one task often transfer to only loosely related tasks, such as from classification to saliency prediction [33], emotion recognition [34], medical imaging [35] and a large number of other transfer learning tasks [36].”
A survey on deep learning in medical image analysis (2017, background): “The topic is now dominant at major conferences and a ﬁrst special issue appeared of IEEE Transaction on Medical Imaging in May 2016 (Greenspan et al., 2016).”
Synthetic data augmentation using GAN for improved liver lesion classification (2018, background): “Collecting medical data is a complex and expensive procedure that requires the collaboration of researchers and radiologists [1].”
ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases (2017, background): “Recent notable work includes (but do not limit to) an overview review on the future promise of deep learning [14] and a collection of important medical applications on lymph node and interstitial lung disease detection and classification [37, 43]; cerebral microbleed detection [11]; pulmonary nodule”
Deep Learning for Health Informatics (2017, background): “The fact that CNNs in medical imaging have yielded promising results have also been highlighted in a recent survey of CNN approaches in brain pathology segmentation [58] and in an editorial of deep learning techniques in computer aided detection, segmentation, and shape analysis [76].”
Leveraging uncertainty information from deep neural networks for disease detection (2016, methodology): “p(y∗|X,y,x∗)≈ ∫ p(y∗|x∗,ω)q(ω)dω (6) We then approximate the integral by Monte Carlo sampling and compute the predictive mean (to be used for a final prediction 350 on a test image): 351 μpred ≈ 1 T T ∑ t=1 p(y|x,θ(ω̂t)) (7) as well as the predictive standard deviation as a proxy for the uncertainty”
Predicting cancer outcomes from histology and genomics using convolutional networks (2017, background): “The ability of CNNs to learn predictive features from raw image data is a paradigm shift that presents exciting opportunities in medical imaging (13 – 15).”
Deep learning approaches for detection and removal of ghosting artifacts in MR spectroscopy (2018, background): “The D-CNNs especially have greatly influenced many fields in medical imaging, and they often show better performance than most state-of-art techniques (17).”
Deep learning in medical imaging and radiation therapy. (2018, background): “Investigations accelerated not only in traditional machine learning topics such as segmentation, lesion detection, and classification 58 but also in other areas such as image reconstruction and artifact reduction that were previously not considered as data-driven topics of investigation.”
Comparison of Deep Transfer Learning Strategies for Digital Pathology (2018, background): “More recently, researchers have been working at applying those new techniques to biomedical imaging [12, 23].”
Adversarial Deep Structural Networks for Mammographic Mass Segmentation (2016, background): “on rely heavily on elaborate human designed features. Model-based methods build classiﬁers and learn the features from the mass [3,4]. There are few works using deep networks to process the mammogram [12,9,23]. Dhungel et al. employed multiple deep belief networks (DBNs), GMM classiﬁer and a priori ”
Detection of coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine (2020, background): “The advancement of machine learning has a great benefit for clinical decision making and development computer-aided systems (Greenspan et al., 2016; Wahab et al., 2017; Xia et al., 207; Burlina et al., 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 ).”
DP-CGAN: Differentially Private Synthetic Data and Label Generation (2019, background): “DNNs have also been leveraged in health-related studies ranging from medical images [22, 39, 38, 21, 4] to human genome analyses [3, 29, 45].”
Deep learning in bioinformatics (2016, background): “[24] discussed deep learning applications in bioinformatics research, the former two are limited to applications in genomic medicine, and the latter to medical imaging.”
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020, background): “There are many survey papers on DL-based key technologies for medical image analysis [22]–[31].”