Quantitative Impact of Label Noise on the Quality of Segmentation of Brain Tumors on MRI scans

Michał Marcinkiewicz; Grzegorz Mrukwa

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arXiv · 1909.08959

Quantitative Impact of Label Noise on the Quality of Segmentation of Brain Tumors on MRI scans

Michał Marcinkiewicz·Grzegorz Mrukwa

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Abstract

Over the last few years, deep learning has proven to be a great solution to many problems, such as image or text classification. Recently, deep learning-based solutions have outperformed humans on selected benchmark datasets, yielding a promising future for scientific and real-world applications. Training of deep learning models requires vast amounts of high quality data to achieve such supreme performance. In real-world scenarios, obtaining a large, coherent, and properly labeled dataset is a challenging task. This is especially true in medical applications, where high-quality data and annotations are scarce and the number of expert annotators is limited. In this paper, we investigate the impact of corrupted ground-truth masks on the performance of a neural network for a brain tumor segmentation task. Our findings suggest that a) the performance degrades about 8% less than it could be expected from simulations, b) a neural network learns the simulated biases of annotators, c) biases can be partially mitigated by using an inversely-biased dice loss function.

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eess.IV cs.CV cs.LG

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Michał Marcinkiewicz, Grzegorz Mrukwa. 2019-09-18. Quantitative Impact of Label Noise on the Quality of Segmentation of Brain Tumors on MRI scans. https://arxiv.org/abs/1909.08959

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