Impact of Annotation Noise on Histopathology Nucleus Segmentation

作     者：Schilling, Marcel P. Ahuja, Niket Rettenberger, Luca Scherr, Tim Reischl, Markus 

作者机构：Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen 76344 Germany 

出 版 物：《Current Directions in Biomedical Engineering》 (Curr. Dir. Biomed. Eng.)

年 卷 期：2022年第8卷第2期

页      面：197-200页

核心收录：

主　　题：Deep neural networks 

摘      要：Deep learning is often used for automated diagnosis support in biomedical image processing scenarios. Annotated datasets are essential for the supervised training of deep neural networks. The problem of consistent and noise-free annotation remains for experts such as pathologists. The variability within an annotator (intra) and the variability between annotators (inter) are current challenges. In clinical practice or biology, instance segmentation is a common task, but a comprehensive and quantitative study regarding the impact of noisy annotations lacks. In this paper, we present a concept to categorize and simulate various types of annotation noise as well as an evaluation of the impact on deep learning pipelines. Thereby, we use the multi-organ histology image dataset MoNuSeg to discuss the influence of annotator variability. We provide annotation recommendations for clinicians to achieve high-quality automated diagnostic algorithms. © 2022 The Author(s), published by De Gruyter.