Ordinal Regression with Neuron Stick-Breaking for Medical Diagnosis

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.