持久性条形码:减少放射分析偏差的新方法。

Q2 Medicine Oncotarget Pub Date : 2024-11-12 DOI:10.18632/oncotarget.28667
Yashbir Singh, Colleen Farrelly, Quincy A Hathaway, Gunnar Carlsson
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引用次数: 0

摘要

持久性条形码作为一种有前途的放射学分析工具,为减少偏差和揭示医学成像中的隐藏模式提供了一种新方法。通过利用拓扑数据分析,该技术为图像特征提供了稳健的多尺度视角,有可能克服传统方法和图神经网络的局限性。虽然在解释和实施方面仍存在挑战,但在不断发展的放射学领域,持久性条形码在提高诊断准确性、标准化以及最终改善患者预后方面显示出巨大的潜力。
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Persistence barcodes: A novel approach to reducing bias in radiological analysis.

Persistence barcodes emerge as a promising tool in radiological analysis, offering a novel approach to reduce bias and uncover hidden patterns in medical imaging. By leveraging topological data analysis, this technique provides a robust, multi-scale perspective on image features, potentially overcoming limitations in traditional methods and Graph Neural Networks. While challenges in interpretation and implementation remain, persistence barcodes show significant potential for improving diagnostic accuracy, standardization, and ultimately, patient outcomes in the evolving field of radiology.

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来源期刊
Oncotarget
Oncotarget Oncogenes-CELL BIOLOGY
CiteScore
6.60
自引率
0.00%
发文量
129
审稿时长
1.5 months
期刊介绍: Information not localized
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