Distributionally Robust Deep Learning using Hardness Weighted Sampling

• DOI: 10.59275/j.melba.2022-8b6a
• Publication Date: 2023-05-17T14:08:20Z
• Authors: Lucas Fidon; Michael Aertsen; Thomas Deprest; Doaa Emam; Frédéric Guffens; Nada Mufti; Esther Van Elslander; Ernst Schwartz; Michael Ebner; Daniela Prayer; Gregor Kasprian; Anna L David; Andrew Melbourne; Sébastien Ourselin; Jan Deprest; Georg Langs; Tom Vercauteren
• Keywords: Machine Learning; FOS: Computer and information sciences; Computer Science - Machine Learning; 03 medical and health sciences; 0302 clinical medicine; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition; Distributionally Robust Optimization; Image Segmentation; Machine Learning (cs.LG)

Limiting failures of machine learning systems is of paramount importance for safety-critical applications. In order to improve the robustness of machine learning systems, Distributionally Robust Optimization (DRO) has been proposed as a generalization of Empirical Risk Minimization (ERM). However, its use in deep learning has been severely restricted due to the relative inefficiency of the optimizers available for DRO in comparison to the wide-spread variants of Stochastic Gradient Descent (SGD) optimizers for ERM.<br>We propose SGD with hardness weighted sampling, a principled and efficient optimization method for DRO in machine learning that is particularly suited in the context of deep learning. Similar to a hard example mining strategy in practice, the proposed algorithm is straightforward to implement and computationally as efficient as SGD-based optimizers used for deep learning, requiring minimal overhead computation. In contrast to typical ad hoc hard mining approaches, we prove the convergence of our DRO algorithm for over-parameterized deep learning networks with ReLU activation and finite number of layers and parameters.<br>Our experiments on fetal brain 3D MRI segmentation and brain tumor segmentation in MRI demonstrate the feasibility and the usefulness of our approach. Using our hardness weighted sampling for training a state-of-the-art deep learning pipeline leads to improved robustness to anatomical variabilities in automatic fetal brain 3D MRI segmentation using deep learning and to improved robustness to the image protocol variations in brain tumor segmentation.a decrease of 2% of the interquartile range of the Dice scores for the enhanced tumor and the tumor core regions.<br>Our code is available at <a href='https://github.com/LucasFidon/HardnessWeightedSampler'>https://github.com/LucasFidon/HardnessWeightedSampler</a>