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Literature DB >> 29736024

AI for medical imaging goes deep.

Daniel S W Ting^1,2, Yong Liu³, Philippe Burlina⁴, Xinxing Xu³, Neil M Bressler⁵, Tien Y Wong⁶.

Abstract

Mesh：

Year: 2018 PMID： 29736024 DOI： 10.1038/s41591-018-0029-3

Source DB: PubMed Journal: Nat Med ISSN： 1078-8956 Impact factor: 53.440

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34 in total

1. Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular Degeneration.

Authors: Philippe M Burlina; Neil Joshi; Katia D Pacheco; T Y Alvin Liu; Neil M Bressler
Journal: JAMA Ophthalmol Date: 2019-03-01 Impact factor: 7.389

Review 2. Enabling Technologies for Personalized and Precision Medicine.

Authors: Dean Ho; Stephen R Quake; Edward R B McCabe; Wee Joo Chng; Edward K Chow; Xianting Ding; Bruce D Gelb; Geoffrey S Ginsburg; Jason Hassenstab; Chih-Ming Ho; William C Mobley; Garry P Nolan; Steven T Rosen; Patrick Tan; Yun Yen; Ali Zarrinpar
Journal: Trends Biotechnol Date: 2020-01-21 Impact factor: 19.536

3. Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy.

Authors: Michelle Y T Yip; Gilbert Lim; Zhan Wei Lim; Quang D Nguyen; Crystal C Y Chong; Marco Yu; Valentina Bellemo; Yuchen Xie; Xin Qi Lee; Haslina Hamzah; Jinyi Ho; Tien-En Tan; Charumathi Sabanayagam; Andrzej Grzybowski; Gavin S W Tan; Wynne Hsu; Mong Li Lee; Tien Yin Wong; Daniel S W Ting
Journal: NPJ Digit Med Date: 2020-03-23

4. Low-Shot Deep Learning of Diabetic Retinopathy With Potential Applications to Address Artificial Intelligence Bias in Retinal Diagnostics and Rare Ophthalmic Diseases.

Authors: Philippe Burlina; William Paul; Philip Mathew; Neil Joshi; Katia D Pacheco; Neil M Bressler
Journal: JAMA Ophthalmol Date: 2020-10-01 Impact factor: 7.389

AI for medical imaging goes deep.

1. Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular Degeneration.

Review 2. Enabling Technologies for Personalized and Precision Medicine.

3. Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy.

4. Low-Shot Deep Learning of Diabetic Retinopathy With Potential Applications to Address Artificial Intelligence Bias in Retinal Diagnostics and Rare Ophthalmic Diseases.

5. An Ophthalmologist's Guide to Deciphering Studies in Artificial Intelligence.

6. Detecting Anomalies in Retinal Diseases Using Generative, Discriminative, and Self-supervised Deep Learning.

7. Self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation.

Review 8. High-performance medicine: the convergence of human and artificial intelligence.

Review 9. Machine learning in optical coherence tomography angiography.

Review 10. Digital Technology-Based Telemedicine for the COVID-19 Pandemic.