Abstract
Supervised deep learning methods offer the potential for automating lesion segmentation in routine clinical brain imaging, but performance is dependent on label quality. In practice, obtaining high-quality labels from experienced annotators for large-scale datasets is not always feasible, while noisy labels from less experienced annotators are often available. Prior studies focus on either label refinement methods or on learning to segment with noisy labels, but there has been little work on integrating these approaches within a unified framework. To address this gap, we propose a novel multitask framework for end-to-end noisy-label refinement and lesion segmentation. Our approach minimizes the discrepancy between the refined label and the predicted segmentation mask, and is highly customizable for scenarios with multiple sets of noisy labels, incomplete ground truth coverage and/or 2D/3D scans. In extensive experiments on both proprietary and public clinical brain imaging datasets, we demonstrate that our end-to-end framework offers strong performance improvements over prevailing baselines on both label refinement and lesion segmentation. Our proposed framework maintains performance gains over baselines even when ground truth labels are available for only 25–50% of the dataset. Our approach has implications for effective medical image segmentation in settings that are replete with noisy labels but sparse on ground truth annotation.
Y. Yu and J. Wang—Contributed equally to this work.
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Acknowledgements
Research efforts were supported by funding and infrastructure for deep learning and medical imaging research from the Institute for Infocomm Research, Science and Engineering Research Council, A*STAR, Singapore and the National Neuroscience Institute, Singapore.
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Yu, Y. et al. (2023). A Multitask Framework for Label Refinement and Lesion Segmentation in Clinical Brain Imaging. In: Xue, Z., et al. Medical Image Learning with Limited and Noisy Data. MILLanD 2023. Lecture Notes in Computer Science, vol 14307. Springer, Cham. https://doi.org/10.1007/978-3-031-44917-8_6
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