Recent technical development of artificial intelligence for diagnostic medical imaging

Deep learning has caused a third boom of artificial intelligence and great changes of diagnostic medical imaging systems such as radiology, pathology, retinal imaging, dermatology inspection, and endoscopic diagnosis will be expected in the near future. However, various attempts and new methods of deep learning have been proposed in recent years, and their progress is extremely fast. Therefore, at the initial stage when medical artificial intelligence papers were published, the artificial intelligence technology itself may be old technology or well-known general-purpose common technology. Therefore, the author has reviewed state-of-the-art computer vision papers and presentations of 2018 using deep learning technologies, which will have future clinical potentials selected from the point of view of a radiologist such as generative adversarial network, knowledge distillation, and general image data sets for supervised learning.

[1]  Yuan Zhou,et al.  AGD: Aneurysm Gene Database , 2018, Database J. Biol. Databases Curation.

[2]  Christian Theobalt,et al.  GANerated Hands for Real-Time 3 D Hand Tracking from Monocular RGB — Extended , 2018 .

[3]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[5]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[6]  Alexei A. Efros,et al.  Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[7]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[8]  Jonathon Shlens,et al.  Conditional Image Synthesis with Auxiliary Classifier GANs , 2016, ICML.

[9]  John E. Hopcroft,et al.  Stacked Generative Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Jaakko Lehtinen,et al.  Progressive Growing of GANs for Improved Quality, Stability, and Variation , 2017, ICLR.

[11]  Antonio Torralba,et al.  LabelMe: A Database and Web-Based Tool for Image Annotation , 2008, International Journal of Computer Vision.

[12]  Yoshua Bengio,et al.  FitNets: Hints for Thin Deep Nets , 2014, ICLR.

[13]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Jung-Woo Ha,et al.  StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[15]  Geoffrey E. Hinton,et al.  Distilling the Knowledge in a Neural Network , 2015, ArXiv.

[16]  Jana Kosecka,et al.  Qualitative image based localization in indoors environments , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[17]  Andrea Esuli,et al.  CoPhIR: a Test Collection for Content-Based Image Retrieval , 2009, ArXiv.

[18]  Ramakant Nevatia,et al.  Knowledge Concentration: Learning 100K Object Classifiers in a Single CNN , 2017, ArXiv.

[19]  Deva Ramanan,et al.  Efficiently Scaling up Crowdsourced Video Annotation , 2012, International Journal of Computer Vision.

[20]  Kenneth R. Koedinger,et al.  A Machine Learning Approach for Automatic Student Model Discovery , 2011, EDM.