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

Holmberg, Olle; Köhler, Niklas; Martins, Thiago Gonçalves dos Santos; Siedlecki, Jakob; Herold, Tina; Keidel, Leonie; Asani, Ben; Schiefelbein, Johannes; Priglinger, Siegfried; Kortuem, Karsten; Theis, Fabian J.

doi:10.1038/s42256-020-00247-1

Cited by 66 publications

(44 citation statements)

References 39 publications

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“…After close inspection of all combinations (Figure 2 only shows the top-5 combination), we observed that SimCLR showed to be more effective than ImageNet in combination with supervised learning. This observation is in alignment with recent papers that show that SimCLR or other self-supervised methods outperform ImageNet on biomedical applications 34,35,36 . However, our analysis showed that ImageNet and SimCLR pre-training performed comparatively similar when being combined with a semi-supervised method.…”

Section: Discussionsupporting

confidence: 90%

Systematic comparison of incomplete-supervision approaches for biomedical imaging classification

Boushehri

Qasim

Waibel

et al. 2020

Preprint

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Deep learning image classification algorithms typically require large annotated datasets. In contrast to real world images where labels are typically cheap and easy to get, biomedical applications require experts’ time for annotation, which is often expensive and scarce. Therefore, identifying methods to maximize performance with a minimal amount of annotation is crucial. A number of active learning algorithms address this problem and iteratively identify most informative images for annotation from the data. However, they are mostly benchmarked on natural image datasets and it is not clear how they perform on biomedical image data with strong class imbalance, little color variance and high similarity between classes. Moreover, active learning neglects the typically abundant unlabeled data available.In this paper, we thus explore strategies combining active learning with pre-training and semi-supervised learning to increase performance on biomedical image classification tasks. We first benchmarked three active learning algorithms, three pre-training methods, and two training strategies on a dataset containing almost 20,000 white blood cell images, split up into ten different classes. Both pre-training using self-supervised learning and pre-trained ImageNet weights boosts the performance of active learning algorithms. A further improvement was achieved using semi-supervised learning. An extensive grid-search through the different active learning algorithms, pre-training methods and training strategies on three biomedical image datasets showed that a specific combination of these methods should be used. This recommended strategy improved the results over conventional annotation-efficient classification strategies by 3% to 14% macro recall in every case. We propose this strategy for other biomedical image classification tasks and expect to boost performance whenever scarce annotation is a problem.

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Section: Discussionsupporting

confidence: 90%

Systematic comparison of incomplete-supervision approaches for biomedical imaging classification

Boushehri

Qasim

Waibel

et al. 2020

Preprint

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“…These research gaps are the primary motivations of the proposal in this article. Firstly, all the cited works uses the supervised learning (SL) scheme to train the models which is known to require massive amount of data to accomplish 39 . Even in the scenarios where adequate data is available the training time for DL models typically takes many hours or days to complete 32 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model

Hannan

How

Lipu

et al. 2021

Sci Rep

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Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning transformer model achieves the lowest root-mean-square-error (RMSE) of 0.90% and a mean-absolute-error (MAE) of 0.44% at constant ambient temperature, and RMSE of 1.19% and a MAE of 0.7% at varying ambient temperature. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell.

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“…Transfer learning has emerged as a machine learning paradigm for such scenarios (Pan and Yang 2010;Neyshabur, Sedghi, and Zhang 2020), where we have access to different datasets from multiple resources (known as source domains) and want to make predictions for a dataset of interest (known as target domain) and it has been employed in different problems (Taroni et al 2019;Raghu et al 2019;Holmberg et al 2020;Hu et al 2020). Various methods of transfer learning have been proposed in the context of drug response prediction.…”

Section: Introductionmentioning

confidence: 99%

Out-of-Distribution Generalization from Labeled and Unlabeled Gene Expression Data for Drug Response Prediction

Sharifi-Noghabi

Harjandi

Zolotareva

et al. 2021

Preprint

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Data discrepancy between preclinical and clinical datasets poses a major challenge for accurate drug response prediction based on gene expression data. Different methods of transfer learning have been proposed to address this data discrepancy. These methods generally use cell lines as source domains and patients, patient-derived xenografts, or other cell lines as target domains. However, they assume that they have access to the target domain during training or fine-tuning and they can only take labeled source domains as input. The former is a strong assumption that is not satisfied during deployment of these models in the clinic. The latter means these methods rely on labeled source domains which are of limited size. To avoid this assumption, we formulate drug response prediction as an out-of-distribution generalization problem which does not assume that the target domain is accessible during training. Moreover, to exploit unlabeled source domain data, which tends to be much more plentiful than labeled data, we adopt a semi-supervised approach. We propose Velodrome, a semi-supervised method of out-of-distribution generalization that takes labeled and unlabeled data from different resources as input and makes generalizable predictions. Velodrome achieves this goal by introducing an objective function that combines a supervised loss for accurate prediction, an alignment loss for generalization, and a consistency loss to incorporate unlabeled samples. Our experimental results demonstrate that Velodrome outperforms state-of-the-art pharmacogenomics and transfer learning baselines on cell lines, patient-derived xenografts, and patients and therefore, may guide precision oncology more accurately.

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

Cited by 66 publications

References 39 publications

Systematic comparison of incomplete-supervision approaches for biomedical imaging classification

Systematic comparison of incomplete-supervision approaches for biomedical imaging classification

Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model

Out-of-Distribution Generalization from Labeled and Unlabeled Gene Expression Data for Drug Response Prediction

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