The classification for medical diagnosis usually involves inherently ordered labels corresponding to the level of health risk. Previous multi-task classifiers on ordinal data often use several binary classification branches to compute a series of cumulative probabilities. However, these cumulative probabilities are not guaranteed to be monotonically decreasing. It also introduces a large number of hyper-parameters to be fine-tuned manually. This paper aims to eliminate or at least largely reduce the effects of those problems. We propose a simple yet efficient way to rephrase the output layer of the conventional deep neural network. We show that our methods lead to the state-of-the-art accuracy on Diabetic Retinopathy dataset and Ultrasound Breast dataset with very little additional cost.
CITATION STYLE
Liu, X., Zou, Y., Song, Y., Yang, C., You, J., & Kumar, B. V. K. V. (2019). Ordinal regression with neuron stick-breaking for medical diagnosis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11134 LNCS, pp. 335–344). Springer Verlag. https://doi.org/10.1007/978-3-030-11024-6_23
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