基于区域的证据深度学习,量化不确定性,提高脑肿瘤分割的稳健性。

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2022-11-17 DOI:10.1007/s00521-022-08016-4
Hao Li, Yang Nan, Javier Del Ser, Guang Yang
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引用次数: 0

摘要

尽管最近在脑肿瘤分割的准确性方面取得了进展,但结果仍然存在可靠性和稳健性低的问题。不确定性估计是这个问题的有效解决方案,因为它提供了分割结果的置信度度量。目前基于分位数回归、贝叶斯神经网络、集成和蒙特卡罗丢弃的不确定性估计方法由于计算成本高和不一致性而受到限制。为了克服这些挑战,证据深度学习(EDL)在最近的工作中得到了发展,但主要用于自然图像分类,并且显示出较差的分割结果。在本文中,我们提出了一种基于区域的EDL分割框架,该框架可以生成可靠的不确定性图和准确的分割结果,对噪声和图像损坏具有鲁棒性。我们使用证据理论将神经网络的输出解释为从输入特征中收集的证据值。根据主观逻辑,证据被参数化为狄利克雷分布,预测的概率被视为主观意见。为了评估我们的模型在分割和不确定性估计方面的性能,我们在BraTS 2020数据集上进行了定量和定性实验。结果证明了所提出的方法在量化分割不确定性和稳健分割肿瘤方面的最高性能。此外,我们提出的新框架保持了计算成本低、易于实现的优势,并显示出临床应用的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation.

Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation.

Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation.

Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation.

Despite recent advances in the accuracy of brain tumor segmentation, the results still suffer from low reliability and robustness. Uncertainty estimation is an efficient solution to this problem, as it provides a measure of confidence in the segmentation results. The current uncertainty estimation methods based on quantile regression, Bayesian neural network, ensemble, and Monte Carlo dropout are limited by their high computational cost and inconsistency. In order to overcome these challenges, Evidential Deep Learning (EDL) was developed in recent work but primarily for natural image classification and showed inferior segmentation results. In this paper, we proposed a region-based EDL segmentation framework that can generate reliable uncertainty maps and accurate segmentation results, which is robust to noise and image corruption. We used the Theory of Evidence to interpret the output of a neural network as evidence values gathered from input features. Following Subjective Logic, evidence was parameterized as a Dirichlet distribution, and predicted probabilities were treated as subjective opinions. To evaluate the performance of our model on segmentation and uncertainty estimation, we conducted quantitative and qualitative experiments on the BraTS 2020 dataset. The results demonstrated the top performance of the proposed method in quantifying segmentation uncertainty and robustly segmenting tumors. Furthermore, our proposed new framework maintained the advantages of low computational cost and easy implementation and showed the potential for clinical application.

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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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