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Self-supervised retinal thickness prediction enables deep learning from unlabelled data to boost classification of diabetic retinopathy

A preprint version of the article is available at bioRxiv.

Abstract

Access to large, annotated samples represents a considerable challenge for training accurate deep-learning models in medical imaging. Although at present transfer learning from pre-trained models can help with cases lacking data, this limits design choices and generally results in the use of unnecessarily large models. Here we propose a self-supervised training scheme for obtaining high-quality, pre-trained networks from unlabelled, cross-modal medical imaging data, which will allow the creation of accurate and efficient models. We demonstrate the utility of the scheme by accurately predicting retinal thickness measurements based on optical coherence tomography from simple infrared fundus images. Subsequently, learned representations outperformed advanced classifiers on a separate diabetic retinopathy classification task in a scenario of scarce training data. Our cross-modal, three-stage scheme effectively replaced 26,343 diabetic retinopathy annotations with 1,009 semantic segmentations on optical coherence tomography and reached the same classification accuracy using only 25% of fundus images, without any drawbacks, since optical coherence tomography is not required for predictions. We expect this concept to apply to other multimodal clinical imaging, health records and genomics data, and to corresponding sample-starved learning problems.

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Fig. 1: Cross-modal self-supervision workflow.
Fig. 2: Accurate device-agnostic OCT thickness segmentation.
Fig. 3: DeepRT consistently predicted retinal thickness across devices based on fundus images.
Fig. 4: Inferring the gold standard from answers with or without access to a predicted thickness map, demonstrating that the proposed method increased the diagnostic capability of physicians.
Fig. 5: SSL reduces the need for annotated samples four-fold.

Data availability

Since this project uses private patient information the data necessary for reproducing all results cannot be shared. For OCT tissue segmentation, the OCT images and their annotations used for tissue segmentation will be made public and accessible through the public code repository. See thickness_segmentation_data.tar.gz at https://zenodo.org/record/3626020#.X2xYc3ZfjCI44. For the thickness map calculation and prediction, two example DICOM files with OCT volumes together with matching infrared fundus images are provided in the public code repository. This allows simple examples to be carried out and readers can see how the input and output data of the different software packages function. Owing to their data privacy policy, the LMU eye clinic is not able to provide access to the full dataset of 121,985 infrared fundus images and OCT volumes outside of the collaboration between the eye hospital and Helmholtz Zentrum München. Although this inhibits the reproducibility of the thickness prediction and screening evaluation results, the weights from the DeepRT model are shared, enabling full reproducibility of the transfer learning onto the Diabetic Retinopathy dataset. The weights and an example partition of the public diabetic retinopathy detection dataset are made available in the code repository. To reproduce the full study of transfer learning properties of DeepRT compared to ImageNet initialization download the full public Color Fundus Kaggle Diabetic Retinopathy dataset at https://www.kaggle.com/c/diabetic-retinopathy-detection/data. Readers should follow the instructions in the public repository for more detailed steps to reproduce the study. https://www.kaggle.com/c/diabetic-retinopathy-detection/data. The labels for the entire training set and test set are provided in the public code repository. For the evaluation, only the public test dataset is used. For the retinal screening evaluation, none of the patient data can be shared and all publicly available information is in the figures.

Code availability

All trained models as well as all code will be open source, enabling reproducibility of the results from the thickness segmentation and transfer learning on diabetic retinopathy. The code used for the above steps is made available through: https://github.com/theislab/DeepRT45.

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Acknowledgements

This work was supported by the BMBF (grant no. 031L0210A) and by Helmholtz Association’s Initiative and Networking Fund through Helmholtz AI (grant no. ZT-I-PF-5-01). We thank the administrative teams at Helmholtz Zentrum Munich and LMU’s University Hospital (LMU-UH) for the fast data-sharing agreement. We thank LMU-UH data protection officer G. Meyer for constructive and fast approval of data processing. We acknowledge the previous work of M. Müller for setting up and maintaining the data warehouse at LMU-UH and A. Anschütz for its continuation. We thank R. Wolff and A. Babenko for creating the electronic medical records in our hospital and C. Kern for the medical oversight. We thank M. Rohm and I. Manakov for discussions on our data.

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Authors

Contributions

O.G.H. developed the deep learning models and the data analysis pipeline. F.J.T. and N.D.K. conceived the study. K.U.K. led the data acquisition and data interpretation. T.M., J.S., T.H., L.K., B.A. and J.S. performed image annotations and screening evaluations. N.K. supervised the study with F.J.T. and K.U.K. F.J.T., N.D.K., O.G.H. and K.U.K. wrote the paper. O.G.H., N.D.K., K.U.K. and F.J.T. contributed to the interpretation of the results. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Karsten U. Kortuem or Fabian J. Theis.

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Competing interests

F.J.T. reports receiving consulting fees from Roche Diagnostics GmbH and Cellarity Inc., and ownership interest in Cellarity, Inc. and Dermagnostix. N.D.K. reports ownership interest in Hellsicht GmbH. The remaining authors declare no competing interests.

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Supplementary Figs. 6–15, Supplementary Discussion and Supplementary Table 1–3

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Holmberg, O.G., Köhler, N.D., Martins, T. et al. Self-supervised retinal thickness prediction enables deep learning from unlabelled data to boost classification of diabetic retinopathy. Nat Mach Intell 2, 719–726 (2020). https://doi.org/10.1038/s42256-020-00247-1

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