# Difficulty-aware Meta-learning for Rare Disease Diagnosis

arXiv: https://arxiv.org/abs/1907.00354 · HTML: https://mlsanity.com/paper/1907.00354/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2019.

## Built on by (most cited first, 24 of 24)

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- [A Unified Framework for Generalized Low-Shot Medical Image Segmentation with Scarce Data](https://mlsanity.com/paper/2110.09260/) (2021)
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- [Robustly Optimized Deep Feature Decoupling Network for Fatty Liver Diseases Detection](https://mlsanity.com/paper/2406.17338/) (2024)
- [FreeEagle: Detecting Complex Neural Trojans in Data-Free Cases](https://mlsanity.com/paper/2302.14500/) (2023)
- [Improving Generalization via Meta-Learning on Hard Samples](https://mlsanity.com/paper/2403.12236/) (2024)
- [Unsupervised Representation Learning Meets Pseudo-Label Supervised Self-Distillation: A New Approach to Rare Disease Classification](https://mlsanity.com/paper/2110.04558/) (2021)
- [One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution Prediction](https://mlsanity.com/paper/2110.11238/) (2021)
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- [Joint Salient Object Detection and Camouflaged Object Detection via Uncertainty-aware Learning](https://mlsanity.com/paper/2307.04651/) (2023)
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- [Data-Efficient and Robust Task Selection for Meta-Learning](https://mlsanity.com/paper/2405.07083/) (2024)
- [Semi Supervised Learning For Few-shot Audio Classification By Episodic Triplet Mining](https://mlsanity.com/paper/2102.08074/) (2021)
- [A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint](https://mlsanity.com/paper/2110.03535/) (2021)
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- [Predicting Brain Multigraph Population From a Single Graph Template for Boosting One-Shot Classification](https://mlsanity.com/paper/2209.06005/) (2022)
- [Multi-task Explainable Skin Lesion Classification](https://mlsanity.com/paper/2310.07209/) (2023)
- [Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution](https://mlsanity.com/paper/2404.16814/) (2024)
- [Feature-context driven Federated Meta-Learning for Rare Disease Prediction](https://mlsanity.com/paper/2112.14364/) (2021)
- [Learning to generate imaginary tasks for improving generalization in meta-learning](https://mlsanity.com/paper/2206.04335/) (2022)

## Abstract (arXiv)

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.