https://www.ncbi.nlm.nih.gov/pubmed/30629091
Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular Degeneration.
Author information
- 1
- Johns Hopkins University Applied Physics Laboratory, Baltimore, Maryland.
- 2
- Malone Center for Engineering in Healthcare, Baltimore, Maryland.
- 3
- Retina Division, Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland.
- 4
- Brasilian Center of Vision Eye Hospital, Brasilia, Distrito Federal, Brazil.
- 5
- Editor.
Abstract
IMPORTANCE:
Deep learning (DL) used for discriminative tasks in ophthalmology, such as diagnosing diabetic retinopathy or age-related macular degeneration (AMD), requires large image data sets graded by human experts to train deep convolutional neural networks (DCNNs). In contrast, generative DL techniques could synthesize large new data sets of artificial retina images with different stages of AMD. Such images could enhance existing data sets of common and rare ophthalmic diseases without concern for personally identifying information to assist medical education of students, residents, and retinal specialists, as well as for training new DL diagnostic models for which extensive data sets from large clinical trials of expertly graded images may not exist.
OBJECTIVE:
To develop DL techniques for synthesizing high-resolution realistic fundus images serving as proxy data sets for use by retinal specialists and DL machines.
DESIGN, SETTING, AND PARTICIPANTS:
Generative adversarial networks were trained on 133 821 color fundus images from 4613 study participants from the Age-Related Eye Disease Study (AREDS), generating synthetic fundus images with and without AMD. We compared retinal specialists' ability to diagnose AMD on both real and synthetic images, asking them to assess image gradability and testing their ability to discern real from synthetic images. The performance of AMD diagnostic DCNNs (referable vs not referable AMD) trained on either all-real vs all-synthetic data sets was compared.
MAIN OUTCOMES AND MEASURES:
Accuracy of 2 retinal specialists' (T.Y.A.L. and K.D.P.) for diagnosing and distinguishing AMD on real vs synthetic images and diagnostic performance (area under the curve) of DL algorithms trained on synthetic vs real images.
RESULTS:
The diagnostic accuracy of 2 retinal specialists on real vs synthetic images was similar. The accuracy of diagnosis as referable vs nonreferable AMD compared with certified human graders for retinal specialist 1 was 84.54% (error margin, 4.06%) on real images vs 84.12% (error margin, 4.16%) on synthetic images and for retinal specialist 2 was 89.47% (error margin, 3.45%) on real images vs 89.19% (error margin, 3.54%) on synthetic images. Retinal specialists could not distinguish real from synthetic images, with an accuracy of 59.50% (error margin, 3.93%) for retinal specialist 1 and 53.67% (error margin, 3.99%) for retinal specialist 2. The DCNNs trained on real data showed an area under the curve of 0.9706 (error margin, 0.0029), and those trained on synthetic data showed an area under the curve of 0.9235 (error margin, 0.0045).
CONCLUSIONS AND RELEVANCE:
Deep learning-synthesized images appeared to be realistic to retinal specialists, and DCNNs achieved diagnostic performance on synthetic data close to that for real images, suggesting that DL generative techniques hold promise for training humans and machines.
- PMID:
- 30629091
- DOI:
- 10.1001/jamaophthalmol.2018.6156
존스홉킨스 대학.
이전의 안저 판독, 딥러닝 자동화.
당뇨병성 망막병증, 노화관련 황반변성(퇴행)에 대한 진단.
기존의 망막사진을 자료로 학습을 하였으나
이번에는 Deep Generative model 로, "자료를 만들어서" 더 학습하게하고, "의사들을 학습하게 하는데" 이용하고,
망막 전문가들에게, 새로 합성하여 만든 안저사진들은, 실제로 촬영한 안저사진과
구분을 못할 정도로
잘 만들었다고 ( Deep Generative model) 함.
학습데이터의 양과 질이
매우 중요한 Deep learning 에서
특히 좋은 자료가 늘 부족한 의료 영역에서
새로운 획기적인 방법이 제시되는 내용인 듯.
이미지 합성 생성은
ProGAN 을 사용함. (progressively grown generative adversarial networks (ProGANs))
ProGANs achieve high-resolution synthetization (in this study, 512 × 512 pixels) by progres-
sively growing networks G and D; they start with simple networks that perform very
low-resolution (2 × 2–pixel images) generative and discriminative tasks.
Subsequently, these are grown by alternating training and adding new network layers.
With each additional layer, the resolution is doubled (eg, 4 × 4, 8 × 8) allowing the
generation of increasingly higher-resolution images.
2019 JAMA_Opth_ Deep Generative Model.pdf
