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- W4320036923 abstract "Large annotated datasets have been a key component in the success of deep learning. However, annotating medical images is challenging as it requires expertise and a large budget. In particular, annotating different types of cells in histopathology suffer from high inter- and intra-rater variability due to the ambiguity of the task. Under this setting, the relation between annotators’ variability and model performance has received little attention. We present a large-scale study on the variability of cell annotations among 120 board-certified pathologists and how it affects the performance of a deep learning model. We propose a method to measure such variability, and by excluding those annotators with low variability, we verify the trade-off between the amount of data and its quality. We found that naively increasing the data size at the expense of inter-rater variability does not necessarily lead to better-performing models in cell detection. Instead, decreasing the inter-rater variability with the expense of decreasing dataset size increased the model performance. Furthermore, models trained from data annotated with lower inter-labeler variability outperform those from higher inter-labeler variability. These findings suggest that the evaluation of the annotators may help tackle the fundamental budget issues in the histopathology domain." @default.
- W4320036923 created "2023-02-12" @default.
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- W4320036923 date "2023-01-01" @default.
- W4320036923 modified "2023-10-15" @default.
- W4320036923 title "Variability Matters: Evaluating Inter-Rater Variability in Histopathology for Robust Cell Detection" @default.
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- W4320036923 doi "https://doi.org/10.1007/978-3-031-25082-8_37" @default.
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