Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks

By Qi-Yan Dou, Hao Chen, Lequan Yu, Lei Zhao, Jing Qin, Defeng Wang, Vincent C. T. Mok, Lin Shi, P. Heng. 2016

621 citations recorded by the source corpus.

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In Epidemiology

In Neurology

Citations in the corpus

Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks (2016, methodology): “A cascaded model with two stages is proposed in [14] for detecting cerebral microbleeds.”
A survey on deep learning in medical image analysis (2017, methodology): “Teramoto et al. (2016) used a multi-stream CNN o integrate CT and Positron Emission Tomography (PET) data. ou et al. (2016b) used a 3D CNN to find micro-bleeds in brain RI.”
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 [36, 42]; cerebral microbleed detection [11]; pulmonary nodule”
Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration (2018, methodology): “33 The p -value for comparing median read-times was calculated empirically via permutation test repeated one million times.”
AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020, positive): “Alternative approaches directly exploit the 3D data by using architectures that perform 3D convolutions and then train the network from scratch on 3D medical images [122]-[126].”
Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI (2018, methodology): “This strategy has been applied in cerebral microhemorrhage detection using a large dataset of 320 MRI volumes and achieved 93% sensitivity.(78) The classification step, when utilizing deep learning, is”
The present and future of deep learning in radiology. (2019, methodology): “DL has been used in disease classification [97,98], brain cancer classification [99–101], organ segmentation [102–108], haemorrhage detection [109,110], tumor detection [111–116] are some key progresses.”
Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis (2020, background): “During the last decade, the convolutional neural networks (CNNs) have achieved astounding achievements in many aspects of the medical imaging ﬁeld, such as ultrasound [3], [4]; MRI [5], [6]; and CT [7], [8].”
Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT (2019, methodology): “Second, although it is straightforward to design 3D CNN for medical image analysis [31], [35], [36], extending the use of 2D CNN to the analysis of volumetric medical images on a slice-by-slice basis, together with data augmentation, enables us to have more training samples [29], [30], [37], [38].”
Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. (2017, background): “Recent works have extended the CNN framework to complex medical image analysis, such as retinal blood vessels segmentation [39,40], retinal hemorrhage detection [41], brain tumor segmentation [42], and cerebral microbleeds detection [43].”
Deep Learning in Medical Image Analysis (2017, background): “…inspired its use to medical image analysis such as image segmentation (22, 23), image registration (24), image fusion (25), image annotation (26), computer-aided diagnosis and prognosis (27, 28, 29), lesion/landmark detection (30, 31, 32), and microscopic imaging analysis (33, 34), to name a few.”
Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation (2017, methodology): “For instance on brain MR analysis, convolutional neural networks (CNN) have been shown to achieve outstanding performance for various tasks including white matter hyperintensities (WMH) segmentation [2], tumor segmentation [3], microbleed detection [4], and lacune detection [5].”
Deep learning based brain tumor segmentation: a survey (2020, background): “ection aims to detect abnormal tumors or lesion tissues and report the predicted class of each tissue. This returns the bounding box as the detection result and the label as the classiﬁed result [9], [10], [11]. It is worth mentioning that some research works with the words “brain tumor detection” o”
Deep learning in medical imaging and radiation therapy. (2018, methodology): “Since convolution operations are highly optimized, this results in fast processing of the entire volume when using a 3D CNN on volumetric data.(172)”
Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks (2017, background): “Recently, convolutional neural networks (CNNs) with hierarchical feature learning capability have led to breakthroughs in many medical image analysis tasks, including classification [15], [16], detection [17]–[20] and segmentation [21], [22].”