Difficulty-aware Meta-Learning for Rare Disease Diagnosis

By Xiaomeng Li, Lequan Yu, Chi-Wing Fu, P. Heng. 2019

75 citations recorded by the source corpus.

Rare diseases have extremely low-data regimes, unlike common diseases with large amount of available labeled data. Hence, to train a neural network to classify rare diseases with a few per-class data samples is very challenging, and so far, catches very little attention. In this paper, we present a difficulty-aware meta-learning method to address rare disease classifications and demonstrate its capability to classify dermoscopy images. Our key approach is to first train and construct a meta-learning model from data of common diseases, then adapt the model to perform rare disease this http URL achieve this, we develop the difficulty-aware meta-learning method that dynamically monitors the importance of learning tasks during the meta-optimization stage. To evaluate our method, we use the recent ISIC 2018 skin lesion classification dataset, and show that with only five samples per class, our model can quickly adapt to classify unseen classes by a high AUC of 83.3%. Also, we evaluated several rare disease classification results in the public Dermofit Image Library to demonstrate the potential of our method for real clinical practice.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Oncology, Meta & Continual Learning, Medical & Health AI

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Citations in the corpus

ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition (2021, background): “[27] Xiaomeng Li, Lequan Yu, Yueming Jin, Chi-Wing Fu, Lei”