Distributionally Robust Deep Learning using Hardness Weighted Sampling

Lucas FidonShool of Biomedical Engineering & Imaging Sciences, King's College London, Michael AertsenDepartment of Radiology, University Hospitals Leuven, Thomas DeprestDepartment of Radiology, University Hospitals Leuven, Doaa EmamDepartment of Obstetrics and Gynaecology, University Hospitals Leuven, Frédéric GuffensDepartment of Radiology, University Hospitals Leuven, Nada MuftiSchool of Biomedical Engineering & Imaging Sciences, King’s College London, Esther Van ElslanderDepartment of Radiology, University Hospitals Leuven, Ernst SchwartzDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Michael EbnerSchool of Biomedical Engineering & Imaging Sciences, King’s College London, Daniela PrayerDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Gregor KasprianDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Anna L DavidInstitute for Women’s Health, University College London, Andrew MelbourneSchool of Biomedical Engineering & Imaging Sciences, King’s College London, Sébastien OurselinSchool of Biomedical Engineering & Imaging Sciences, King’s College London, Jan DeprestDepartment of Obstetrics and Gynaecology, University Hospitals Leuven, Georg LangsDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Tom VercauterenSchool of Biomedical Engineering & Imaging Sciences, King’s College London
PIPPI 2021 special issue
Publication date: 2022/07/18
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Abstract

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.
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.
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.
Our code is available at https://github.com/LucasFidon/HardnessWeightedSampler

Keywords

Machine Learning · Image Segmentation · Distributionally Robust Optimization

Bibtex@article{melba:2022:019:fidon, title = "Distributionally Robust Deep Learning using Hardness Weighted Sampling", authors = "Fidon, Lucas and Aertsen, Michael and Deprest, Thomas and Emam, Doaa and Guffens, Frédéric and Mufti, Nada and Van Elslander, Esther and Schwartz, Ernst and Ebner, Michael and Prayer, Daniela and Kasprian, Gregor and David, Anna L and Melbourne, Andrew and Ourselin, Sébastien and Deprest, Jan and Langs, Georg and Vercauteren, Tom", journal = "Machine Learning for Biomedical Imaging", volume = "1", issue = "PIPPI 2021 special issue", year = "2022" }

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