Contrastive learning-based pretraining improves representation and
transferability of diabetic retinopathy classification models
- URL: http://arxiv.org/abs/2208.11563v1
- Date: Wed, 24 Aug 2022 14:07:45 GMT
- Title: Contrastive learning-based pretraining improves representation and
transferability of diabetic retinopathy classification models
- Authors: Minhaj Nur Alam, Rikiya Yamashita, Vignav Ramesh, Tejas Prabhune,
Jennifer I. Lim, R.V.P. Chan, Joelle Hallak, Theodore Leng, and Daniel Rubin
- Abstract summary: Self supervised contrastive learning based pretraining allows development of robust and generalized deep learning models with small, labeled datasets.
This paper aims to evaluate the effect of CL based pretraining on the performance of referrable vs non referrable diabetic retinopathy (DR) classification.
- Score: 1.9882302955470608
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Self supervised contrastive learning based pretraining allows development of
robust and generalized deep learning models with small, labeled datasets,
reducing the burden of label generation. This paper aims to evaluate the effect
of CL based pretraining on the performance of referrable vs non referrable
diabetic retinopathy (DR) classification. We have developed a CL based
framework with neural style transfer (NST) augmentation to produce models with
better representations and initializations for the detection of DR in color
fundus images. We compare our CL pretrained model performance with two state of
the art baseline models pretrained with Imagenet weights. We further
investigate the model performance with reduced labeled training data (down to
10 percent) to test the robustness of the model when trained with small,
labeled datasets. The model is trained and validated on the EyePACS dataset and
tested independently on clinical data from the University of Illinois, Chicago
(UIC). Compared to baseline models, our CL pretrained FundusNet model had
higher AUC (CI) values (0.91 (0.898 to 0.930) vs 0.80 (0.783 to 0.820) and 0.83
(0.801 to 0.853) on UIC data). At 10 percent labeled training data, the
FundusNet AUC was 0.81 (0.78 to 0.84) vs 0.58 (0.56 to 0.64) and 0.63 (0.60 to
0.66) in baseline models, when tested on the UIC dataset. CL based pretraining
with NST significantly improves DL classification performance, helps the model
generalize well (transferable from EyePACS to UIC data), and allows training
with small, annotated datasets, therefore reducing ground truth annotation
burden of the clinicians.
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