ST-MetaDiagnosis: Meta learning with Spatial Transform for rare skin disease Diagnosis

Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are seen instead by general practitioners with lower diagnostic accuracy. Current skin disease researches adopt the auto-classification system for improving the accuracy rate of skin disease classification. It is therefore an important task to develop Computer Aided Detection (CAD) systems that can aid/enhance dermatologists workflow and improve the classification performances. However, the long-tailed class distribution in the database and the limitation of ability to achieve a spatially invariant features make this problem challenging. We propose a ST-MetaDiagnosis, which utilizes meta-learning and spatial transform learning to facilitate quick adaptation and generalization of deep neural networks trained on the common diseases data for identification of rare diseases with much less annotated data. In particular, in order to predict the target risk where there are limited data samples, we train a meta-learner with spatial transforming from a set of related risk prediction tasks which learns how a good predictor is learned. The meta-learned can be directly used in target risk prediction, and the limited available samples can be used for further fine-tuning the model performance. Experiments on the recent ISIC 2018 skin lesion classification dataset show that our ST-MetaDiagnosis obtains 64.6% (accuracy) and 64.4% (F1-score) on the diagnosis of actinic keratosis, vascular lesion and dermatofibroma, demonstrating that ST-MetaDiagnosis can improve performance for predicting target risk with low resources comparing with the predictor trained on the limited samples available for this risk.