Diabetic retinopathy (DR) is caused by long-standing diabetes and is among
the fifth leading cause for visual impairments. The process of early diagnosis
and treatments could be helpful in curing the disease, however, the detection
procedure is rather challenging and mostly tedious. Therefore, automated
diabetic retinopathy classification using deep learning techniques has gained
interest in the medical imaging community. Akin to several other real-world
applications of deep learning, the typical assumption of i.i.d data is also
violated in DR classification that relies on deep learning. Therefore,
developing DR classification methods robust to unseen distributions is of great
value. In this paper, we study the problem of generalizing a model to unseen
distributions or domains (a.k.a domain generalization) in DR classification. To
this end, we propose a simple and effective domain generalization (DG) approach
that achieves self-distillation in vision transformers (ViT) via a novel
prediction softening mechanism. This prediction softening is an adaptive convex
combination one-hot labels with the model's own knowledge. We perform extensive
experiments on challenging open-source DR classification datasets under both
multi-source and single-source DG settings with three different ViT backbones
to establish the efficacy and applicability of our approach against competing
methods. For the first time, we report the performance of several
state-of-the-art DG methods on open-source DR classification datasets after
conducting thorough experiments. Finally, our method is also capable of
delivering improved calibration performance than other methods, showing its
suitability for safety-critical applications, including healthcare. We hope
that our contributions would investigate more DG research across the medical
imaging community.Comment: Accepted at WACV 202