减少放射学中的偏差:拓扑数据分析和简单复合物的前景。

Q2 Medicine Oncotarget Pub Date : 2024-11-12 DOI:10.18632/oncotarget.28668
Yashbir Singh, Colleen Farrelly, Quincy A Hathaway, Gunnar Carlsson
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引用次数: 0

摘要

拓扑数据分析(TDA)和简单复合物为解决人工智能辅助放射学中的偏差问题提供了一种新方法。通过捕捉医学影像中的复杂结构、n 向相互作用和几何关系,拓扑数据分析增强了特征提取,提高了表示的鲁棒性,并增加了可解释性。这一数学框架有望显著提高放射评估的准确性和公平性,为更公平的患者护理铺平道路。
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Mitigating bias in radiology: The promise of topological data analysis and simplicial complexes.

Topological Data Analysis (TDA) and simplicial complexes offer a novel approach to address biases in AI-assisted radiology. By capturing complex structures, n-way interactions, and geometric relationships in medical images, TDA enhances feature extraction, improves representation robustness, and increases interpretability. This mathematical framework has the potential to significantly improve the accuracy and fairness of radiological assessments, paving the way for more equitable patient care.

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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
期刊最新文献
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