Learning to Segment from Noisy Annotations: A Spatial Correction Approach

• URL: http://arxiv.org/abs/2308.02498v1
• Date: Fri, 21 Jul 2023 00:27:40 GMT
• Title: Learning to Segment from Noisy Annotations: A Spatial Correction Approach
• Authors: Jiachen Yao, Yikai Zhang, Songzhu Zheng, Mayank Goswami, Prateek Prasanna, Chao Chen
• Abstract summary: Noisy labels can significantly affect the performance of deep neural networks (DNNs) We propose a novel Markov model for segmentation noisy annotations that encodes both spatial correlation and bias. Our approach outperforms current state-of-the-art methods on both synthetic and real-world noisy annotations.
• Score: 12.604673584405385
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Noisy labels can significantly affect the performance of deep neural networks (DNNs). In medical image segmentation tasks, annotations are error-prone due to the high demand in annotation time and in the annotators' expertise. Existing methods mostly assume noisy labels in different pixels are \textit{i.i.d}. However, segmentation label noise usually has strong spatial correlation and has prominent bias in distribution. In this paper, we propose a novel Markov model for segmentation noisy annotations that encodes both spatial correlation and bias. Further, to mitigate such label noise, we propose a label correction method to recover true label progressively. We provide theoretical guarantees of the correctness of the proposed method. Experiments show that our approach outperforms current state-of-the-art methods on both synthetic and real-world noisy annotations.

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