A Hybrid Robust-Learning Architecture for Medical Image Segmentation with Noisy Labels

Jialin Shi, Chenyi Guo and Ji Wu
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Jialin Shi: The Department of Electronic Engineering, Tsinghua University, Beijing 100089, China
Chenyi Guo: The Department of Electronic Engineering, Tsinghua University, Beijing 100089, China
Ji Wu: The Department of Electronic Engineering, Tsinghua University, Beijing 100089, China

Future Internet, 2022, vol. 14, issue 2, 1-13

Abstract: Deep-learning models require large amounts of accurately labeled data. However, for medical image segmentation, high-quality labels rely on expert experience, and less-experienced operators provide noisy labels. How one might mitigate the negative effects caused by noisy labels for 3D medical image segmentation has not been fully investigated. In this paper, our purpose is to propose a novel hybrid robust-learning architecture to combat noisy labels for 3D medical image segmentation. Our method consists of three components. First, we focus on the noisy annotations of slices and propose a slice-level label-quality awareness method, which automatically generates label-quality scores for slices in a set. Second, we propose a shape-awareness regularization loss based on distance transform maps to introduce prior shape information and provide extra performance gains. Third, based on a re-weighting strategy, we propose an end-to-end hybrid robust-learning architecture to weaken the negative effects caused by noisy labels. Extensive experiments are performed on two representative datasets (i.e., liver segmentation and multi-organ segmentation). Our hybrid noise-robust architecture has shown competitive performance, compared to other methods. Ablation studies also demonstrate the effectiveness of slice-level label-quality awareness and a shape-awareness regularization loss for combating noisy labels.

Keywords: deep learning; noisy labels; medical image segmentation; robust learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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