Towards Generalizable Medical Image Segmentation with Pixel-wise Uncertainty Estimation

• URL: http://arxiv.org/abs/2305.07883v3
• Date: Sat, 24 Jun 2023 06:50:14 GMT
• Title: Towards Generalizable Medical Image Segmentation with Pixel-wise Uncertainty Estimation
• Authors: Shuai Wang, Zipei Yan, Daoan Zhang, Zhongsen Li, Sirui Wu, Wenxuan Chen, Rui Li
• Abstract summary: Deep neural networks (DNNs) achieve promising performance in visual recognition under the independent and identically distributed (IID) hypothesis. IID hypothesis is not universally guaranteed in numerous real-world applications, especially in medical image analysis. We propose a novel framework that utilizes uncertainty estimation to highlight hard-to-classified pixels for DNNs.
• Score: 5.415458419290347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks (DNNs) achieve promising performance in visual recognition under the independent and identically distributed (IID) hypothesis. In contrast, the IID hypothesis is not universally guaranteed in numerous real-world applications, especially in medical image analysis. Medical image segmentation is typically formulated as a pixel-wise classification task in which each pixel is classified into a category. However, this formulation ignores the hard-to-classified pixels, e.g., some pixels near the boundary area, as they usually confuse DNNs. In this paper, we first explore that hard-to-classified pixels are associated with high uncertainty. Based on this, we propose a novel framework that utilizes uncertainty estimation to highlight hard-to-classified pixels for DNNs, thereby improving its generalization. We evaluate our method on two popular benchmarks: prostate and fundus datasets. The results of the experiment demonstrate that our method outperforms state-of-the-art methods.

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