Opinions Vary? Diagnosis First!
Junde Wu, Huihui Fang, Dalu Yang, Zhaowei Wang, Wenshuo Zhou, Fangxin, Shang, Yehui Yang, Yanwu Xu

TL;DR
This paper introduces a novel method to fuse multiple expert annotations for optic disc and cup segmentation by leveraging glaucoma diagnosis performance, resulting in improved segmentation accuracy for clinical use.
Contribution
It proposes a new strategy that assesses expert reliability through diagnosis performance and generates a unified ground-truth, enhancing segmentation models.
Findings
Improved glaucoma diagnosis accuracy using the fused ground-truth.
Superior OD/OC segmentation performance with DiagFirstGT.
Effective expert reliability assessment via attentive diagnosis network.
Abstract
With the advancement of deep learning techniques, an increasing number of methods have been proposed for optic disc and cup (OD/OC) segmentation from the fundus images. Clinically, OD/OC segmentation is often annotated by multiple clinical experts to mitigate the personal bias. However, it is hard to train the automated deep learning models on multiple labels. A common practice to tackle the issue is majority vote, e.g., taking the average of multiple labels. However such a strategy ignores the different expertness of medical experts. Motivated by the observation that OD/OC segmentation is often used for the glaucoma diagnosis clinically, in this paper, we propose a novel strategy to fuse the multi-rater OD/OC segmentation labels via the glaucoma diagnosis performance. Specifically, we assess the expertness of each rater through an attentive glaucoma diagnosis network. For each rater,…
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Taxonomy
TopicsRetinal Imaging and Analysis · Glaucoma and retinal disorders · Medical Imaging and Analysis
