A Comprehensive Study of Modern Architectures and Regularization
Approaches on CheXpert5000
- URL: http://arxiv.org/abs/2302.06684v1
- Date: Mon, 13 Feb 2023 20:51:24 GMT
- Title: A Comprehensive Study of Modern Architectures and Regularization
Approaches on CheXpert5000
- Authors: Sontje Ihler, Felix Kuhnke, Svenja Spindeldreier
- Abstract summary: We present a study on medical image classification with limited annotations (5k)
We find that models pretrained on ImageNet21k achieve a higher AUC and larger models require less training steps.
Vision Transformer achieve comparable or on par results to Big Transfer Models.
- Score: 3.7384509727711923
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Computer aided diagnosis (CAD) has gained an increased amount of attention in
the general research community over the last years as an example of a typical
limited data application - with experiments on labeled 100k-200k datasets.
Although these datasets are still small compared to natural image datasets like
ImageNet1k, ImageNet21k and JFT, they are large for annotated medical datasets,
where 1k-10k labeled samples are much more common. There is no baseline on
which methods to build on in the low data regime. In this work we bridge this
gap by providing an extensive study on medical image classification with
limited annotations (5k). We present a study of modern architectures applied to
a fixed low data regime of 5000 images on the CheXpert dataset. Conclusively we
find that models pretrained on ImageNet21k achieve a higher AUC and larger
models require less training steps. All models are quite well calibrated even
though we only fine-tuned on 5000 training samples. All 'modern' architectures
have higher AUC than ResNet50. Regularization of Big Transfer Models with MixUp
or Mean Teacher improves calibration, MixUp also improves accuracy. Vision
Transformer achieve comparable or on par results to Big Transfer Models.
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