# Difficulty-aware Meta-learning for Rare Disease Diagnosis Source: [Difficulty-aware Meta-learning for Rare Disease Diagnosis]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:25:59.816Z Authors: Xiaomeng Li, Lequan Yu, Yueming Jin, Chi-Wing Fu, Lei Xing, Pheng-Ann Heng Published: 2019-06-30T10:18:02.000Z ## Abstract 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 classification.To 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Skin Lesion Classification on ISIC 3-way 1-shot 2018 (test)](): Skin lesion classification; ISIC 3-way 1-shot 2018 (test); Accuracy: 50.05. - [Skin Lesion Classification on ISIC 3-way 3-shot 2018 (test)](): Skin lesion classification; ISIC 3-way 3-shot 2018 (test); Accuracy: 55.57. - [Skin Lesion Classification on ISIC 3-way 5-shot 2018 (test)](): Skin lesion classification; ISIC 3-way 5-shot 2018 (test); Accuracy: 59.44. ## Original sources and implementation [Original paper]() [PDF]()