Large scale deep learning for computer aided detection of mammographic lesions

By Thijs Kooi, Geert J. S. Litjens, B. van Ginneken, A. Gubern-Mérida, Clara I. Sánchez, R. Mann, A. Heeten, Nico Karssemeijer. 2017

912 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

A survey on deep learning in medical image analysis (2017, methodology): “Recently, interest has returned which resulted in signiﬁcant advances over the state of the art, achieving the performance of human readers on ROIs (Kooi et al., 2016).”
Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI (2018, background): “DLMs work with multiple processing layers and levels of abstractions resulting in deep convolutional neural networks that need a larger amount of training data for extraction of a complex hierarchy of features by its self-learning abilities [14, 17, 18].”
The impact of artificial intelligence in medicine on the future role of the physician (2019, methodology): “Kooi et al. (2017) compared a traditional state-of-the art in mammography Computer Aided Detection (CAD) system which relied on a manually designed feature set with a DL Convolutional Neural Network (CNN) algorithm to detect breast cancer lesions.”
The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis (2019, methodology): “To classify medical images, a deep convolutional neural network is first pretrained on natural images, and then is only “fine-tuned” on the relevant clinical data, as has been shown in diabetic retinopathy [20] and computer-aided diagnosis in breast cancer [31].”
Applications of Deep Learning and Reinforcement Learning to Biological Data (2017, methodology): “olynomial network (MM-SDPN) [164]. Due to its facilitating structure, CNN has been the most popular DL architecture for image analysis. CNN was applied to classify breast masses from mammograms (MMM) [7,36,78,88,173], diagnose AD using different neuroimages (e.g., brain MRI [72], brain CT scans [50]”
A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification (2018, methodology): “In this study, we have augmented the original mammograms eight times by rotating them with the angles of = ° 45 (i.e., ° 0 , ° 45 , ° 90 , ° , ° , ° , ° 270 and ° ) [10,19,24,29,33].”
Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology (2018, background): “These statements are best understood from the perspective of advances in a subset of tasks accomplished by radiologists that require specialized intelligence, mainly detection of anomalies [40,53], segmentation [54], and image classification [55].”
PSNet: prostate segmentation on MRI based on a convolutional neural network (2018, methodology): “Kooi et al. 14 proposed a CNN method for breast lesion detection in mammography.”
A guide to deep learning in healthcare (2019, background): “…tasks, including identifying moles from melanomas 9,10 , diabetic retinopathy, cardiovascular risk, and referrals from fundus 15,16 and optical coherence tomography (OCT) 17 images of the eye, breast lesion detection in mammograms 13 , and spinal analysis with magnetic resonance imaging 23 .”
Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks (2018, methodology): “This approach trains the convolutional neural networks (CNNs) with image patches for training and a sliding window approach for testing [20], [21].”
Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions (2017, background): “Deep learning techniques are gaining popularity in many areas of medical image analysis [2], such as computer-aided detection of breast lesions [3], computer-aided diagnosis of breast lesions and pulmonary nodules [4], and in histopathological diagnosis [5].”