Towards Generalizable Medical Image Segmentation with Pixel-wise Uncertainty Estimation
This addresses generalization issues in medical image segmentation for healthcare applications, but it is incremental as it builds on existing uncertainty estimation techniques.
The paper tackles the problem of poor generalization in medical image segmentation due to hard-to-classified pixels, such as those near boundaries, by proposing a framework that uses pixel-wise uncertainty estimation to highlight these pixels, resulting in outperforming state-of-the-art methods on prostate and fundus datasets.
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.