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.

本文通过对只有 5000 个样本图片的 CheXpert 数据集的医学图像分类进行现代架构的比较研究，发现在低数据实验中，以 ImageNet21k 为预训练数据集是可行的，并且较大的模型需要较少的训练步骤。同时，本文的研究验证了 MixUp 或 Mean Teacher 正则化技术能够提高模型的校准性能，其中 MixUp 技术还能提高模型的准确性。