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Deep learning approaches exhibit promising performances on various texttasks. However, they are still struggling on medical text classification sincesamples are often extremely imbalanced and scarce. Different from existingmainstream approaches that focus on supplementary semantics with externalmedical information, this paper aims to rethink the data challenges in medicaltexts and present a novel framework-agnostic algorithm called Text2Tree thatonly utilizes internal label hierarchy in training deep learning models. Weembed the ICD code tree structure of labels into cascade attention modules forlearning hierarchy-aware label representations. Two new learning schemes,Similarity Surrogate Learning (SSL) and Dissimilarity Mixup Learning (DML), aredevised to boost text classification by reusing and distinguishing samples ofother labels following the label representation hierarchy, respectively.Experiments on authoritative public datasets and real-world medical recordsshow that our approach stably achieves superior performances over classical andadvanced imbalanced classification methods.

Text2Tree: Aligning Text Representation to the Label Tree Hierarchy for Imbalanced Medical Classification