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Deep learning techniques are often criticized to heavily depend on a largequantity of labeled data. This problem is even more challenging in medicalimage analysis where the annotator expertise is often scarce. We propose anovel data-augmentation method to regularize neural network regressors thatlearn from a single global label per image. The principle of the method is tocreate new samples by recombining existing ones. We demonstrate the performanceof our algorithm on two tasks: estimation of the number of enlargedperivascular spaces in the basal ganglia, and estimation of white matterhyperintensities volume. We show that the proposed method improves theperformance over more basic data augmentation. The proposed method reached anintraclass correlation coefficient between ground truth and network predictionsof 0.73 on the first task and 0.84 on the second task, only using between 25and 30 scans with a single global label per scan for training. With the samenumber of training scans, more conventional data augmentation methods couldonly reach intraclass correlation coefficients of 0.68 on the first task, and0.79 on the second task.

Hydranet: Data Augmentation for Regression Neural Networks