MedAI Session 11: Hydranet -- Data Augmentation for Regression Neural Networks | Florian Dubost

Title: Hydranet -- Data Augmentation for Regression Neural Networks

Speaker: Florian Dubost

Abstract:
Deep learning techniques are often criticized to heavily depend on a large quantity of labeled data. This problem is even more challenging in medical image analysis where the annotator expertise is often scarce. We propose a novel data-augmentation method to regularize neural network regressors that learn from a single global label per image. The principle of the method is to create new samples by recombining existing ones. We demonstrate the performance of our algorithm on two tasks- estimation of the number of enlarged perivascular spaces in the basal ganglia, and estimation of white matter hyperintensities volume. We show that the proposed method improves the performance over more basic data augmentation. The proposed method reached an intraclass correlation coefficient between ground truth and network predictions of 0.73 on the first task and 0.84 on the second task, only using between 25 and 30 scans with a single global label per scan for training. With the same number of training scans, more conventional data augmentation methods could only reach intraclass correlation coefficients of 0.68 on the first task, and 0.79 on the second task.

Speaker Bio:
Florian Dubost is a postdoctoral researcher in biomedical data science at Stanford University, CA, USA, and has with six years of experience in machine learning. He holds a PhD in medical computer vision and reached top rankings in international deep learning competitions. He is member of program committees at conference workshops in AI and medicine, authored a book in AI and neurology, and is an author and reviewer for top international journals and conferences in AI and medicine with over 20 published articles, including 11 as first author.

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