A survey on deep learning in medical image analysis

Litjens, Geert; Kooi, Thijs; Bejnordi, Babak Ehteshami; Setio, Arnaud Arindra Adiyoso; Ciompi, Francesco; Ghafoorian, Mohsen; Laak, Jeroen van der; Ginneken, Bram van; Sánchez, Clara I.

doi:10.1016/j.media.2017.07.005

Cited by 9,671 publications

(5,913 citation statements)

References 268 publications

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“…single frame, early fusion, late fusion, and slow fusion [6]. The importance of deep learning in medical image analysis and content-based processing and analysis of endoscopic images and video also is apparent from the work of Litjens et al [9] and Muenzer et al [12] respectively.…”

Section: Introductionmentioning

confidence: 99%

Learning laparoscopic video shot classification for gynecological surgery

Petscharnig

Schöffmann

2017

Multimed Tools Appl

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Videos of endoscopic surgery are used for education of medical experts, analysis in medical research, and documentation for everyday clinical life. Hand-crafted image descriptors lack the capabilities of a semantic classification of surgical actions and video shots of anatomical structures. In this work, we investigate how well single-frame convolutional neural networks (CNN) for semantic shot classification in gynecologic surgery work. Together with medical experts, we manually annotate hours of raw endoscopic gynecologic surgery videos showing endometriosis treatment and myoma resection of over 100 patients. The cleaned ground truth dataset comprises 9 h of annotated video material (from 111 different recordings). We use the well-known CNN architectures AlexNet and GoogLeNet and train these architectures for both, surgical actions and anatomy, from scratch. Furthermore, we extract high-level features from AlexNet with weights from a pre-trained model from the Caffe model zoo and feed them to an SVM classifier. Our evaluation shows that we reach an average recall of .697 and .515 for classification of anatomical structures and surgical actions respectively using off-the-shelf CNN features. Using GoogLeNet, we achieve a mean recall of .782 and .617 for classification of anatomical structures and surgical actions respectively. With AlexNet the achieved recall is .615 for anatomical structures and .469 for surgical action classification respectively. The main conclusion of our work is that advances in general image classification methods transfer to the domain of endoscopic surgery videos in gynecology. This is relevant as this domain is different from natural images, e.g. it is distinguished by smoke, reflections, or a limited amount of colors.

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Section: Introductionmentioning

confidence: 99%

Learning laparoscopic video shot classification for gynecological surgery

Petscharnig

Schöffmann

2017

Multimed Tools Appl

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“…Initiatives in medical data analysis show really promising adoptions of deep neural networks. Images are used to diagnose precisely various pathologies [11]. EEG analysis are also a good example when it comes to learning patterns on complex combination of sensors/time-series.…”

Section: Deep Learning For Signal and Image Processingmentioning

confidence: 99%

Pragmatic Big Data and smart manufacturing

Benhenni¹

2017

18th International Congress of Metrology

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Abstract. Smart manufacturing is usually associated with fancy cobots and automated production lines. But the Big Data ecosystem has a part to play, and is probably faster and cheaper to start with. The numerical revolution has dramatically accelerated these last years, with many implications in different industries. We propose in this article to present these new tools, and discuss some real use cases. We will also present the best practices and some of the inevitable challenges related to data projects.The upcoming Industry 4.0 movement is full of promises. However, many still think it is the vision of a distant future, implying heavy changes and new fancy but expensive machines and tools. But it is first and foremost a digital transformation, and as such, will generate its share of data. It will therefore be possible to leverage the Big Data technological ecosystem to: -harness and bring value out of the data, -improve quality and conformance, -automate controls on the production line, -have a tight control on industrial and manufacturing processes as well as on the machinery and tools.

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“…Currently, Deep Learning is used for a great variety of computer science tasks such as image classification, semantic segmentation, object detection and localization, etc. A number of studies has proven the efficiency of utilizing Deep Convolutional Networks in biomedical image analysis tasks (Ravi, D., 2017;Zhou, S., 2017;Litjens, G., 2017.). Several studies accomplished by authors on the use of Convolutional Neural Networks for histology image classification in breast cancer diagnosis (Kovalev, V., 2016b), lung segmentation (Kalinovsky, A.…”

Section: Emergence Of Deep Learningmentioning

confidence: 99%

Lesion Detection in Ct Images Using Deep Learning Semantic Segmentation Technique

Kalinovsky

Liauchuk

Tarasau

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper, the problem of automatic detection of tuberculosis lesion on 3D lung CT images is considered as a benchmark for testing out algorithms based on a modern concept of Deep Learning. For training and testing of the algorithms a domestic dataset of 338 3D CT scans of tuberculosis patients with manually labelled lesions was used. The algorithms which are based on using Deep Convolutional Networks were implemented and applied in three different ways including slice-wise lesion detection in 2D images using semantic segmentation, slice-wise lesion detection in 2D images using sliding window technique as well as straightforward detection of lesions via semantic segmentation in whole 3D CT scans. The algorithms demonstrate superior performance compared to algorithms based on conventional image analysis methods.

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A survey on deep learning in medical image analysis

Cited by 9,671 publications

References 268 publications

Learning laparoscopic video shot classification for gynecological surgery

Learning laparoscopic video shot classification for gynecological surgery

Pragmatic Big Data and smart manufacturing

Lesion Detection in Ct Images Using Deep Learning Semantic Segmentation Technique

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