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Conference Paper: Difficulty-aware meta-learning for rare disease diagnosis

TitleDifficulty-aware meta-learning for rare disease diagnosis
Authors
Issue Date2020
PublisherSpringer.
Citation
23rd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2020), Lima, Peru, 4-8 October 2020. In Martel, AL, Abolmaesumi, P, Stoyanov, D, et al. (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I, p. 357-366. Cham, Switzerland: Springer, 2020 How to Cite?
AbstractRare 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.
Persistent Identifierhttp://hdl.handle.net/10722/299474
ISBN
ISSN
2023 SCImago Journal Rankings: 0.606
Series/Report no.Lecture Notes in Computer Science ; 12261

 

DC FieldValueLanguage
dc.contributor.authorLi, Xiaomeng-
dc.contributor.authorYu, Lequan-
dc.contributor.authorJin, Yueming-
dc.contributor.authorFu, Chi Wing-
dc.contributor.authorXing, Lei-
dc.contributor.authorHeng, Pheng Ann-
dc.date.accessioned2021-05-21T03:34:29Z-
dc.date.available2021-05-21T03:34:29Z-
dc.date.issued2020-
dc.identifier.citation23rd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2020), Lima, Peru, 4-8 October 2020. In Martel, AL, Abolmaesumi, P, Stoyanov, D, et al. (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I, p. 357-366. Cham, Switzerland: Springer, 2020-
dc.identifier.isbn9783030597092-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/299474-
dc.description.abstractRare 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.-
dc.languageeng-
dc.publisherSpringer.-
dc.relation.ispartofMedical Image Computing and Computer Assisted Intervention – MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I-
dc.relation.ispartofseriesLecture Notes in Computer Science ; 12261-
dc.titleDifficulty-aware meta-learning for rare disease diagnosis-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-030-59710-8_35-
dc.identifier.scopuseid_2-s2.0-85093078009-
dc.identifier.spage357-
dc.identifier.epage366-
dc.identifier.eissn1611-3349-
dc.publisher.placeCham, Switzerland-

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