使用CycleGANs进行乳腺密度转换以揭示乳腺X线图中未检测到的发现

Signals Pub Date : 2023-06-01 DOI:10.3390/signals4020022
D. Anyfantis, A. Koutras, G. Apostolopoulos, Ioanna Christoyianni
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引用次数: 0

摘要

癌症是癌症中最常见的癌症,也是导致发病率和死亡率的主要原因,也是世界范围内的一个重大健康问题。根据世界卫生组织癌症意识建议,应定期对中老年妇女进行乳房X光检查,以增加早期发现癌症的机会。众所周知,乳腺密度与癌症发展的风险有关。美国放射学会乳腺成像报告和数据系统根据乳房密度将乳房X光检查分为四个级别,从ACR-A(最不密集)到ACR-D(最密集)。计算机辅助诊断(CAD)系统现在可以检测乳房X光照片中的可疑区域,并比人类读者更快、更准确地识别异常。然而,它们的性能仍然受到组织密度水平的影响,在设计这种系统时必须考虑这一点。在本文中,我们提出了一种新的方法,使用CycleGANs将乳房X光片的可疑区域从ACR-B、-C和-D水平转换为ACR-a水平。这种转化旨在减少厚组织引起的掩蔽效应,并将癌区与周围组织分离。我们提出的系统通过关注感兴趣的区域,显著提高了传统的基于CNN的分类器的性能,否则这些区域会因脂肪掩蔽而被错误识别。对不同类型的乳房X光片(数字和扫描X光片)进行的广泛测试证明了我们的系统在识别正常、良性和恶性感兴趣区域方面的有效性。
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Breast Density Transformations Using CycleGANs for Revealing Undetected Findings in Mammograms
Breast cancer is the most common cancer in women, a leading cause of morbidity and mortality, and a significant health issue worldwide. According to the World Health Organization’s cancer awareness recommendations, mammographic screening should be regularly performed on middle-aged or older women to increase the chances of early cancer detection. Breast density is widely known to be related to the risk of cancer development. The American College of Radiology Breast Imaging Reporting and Data System categorizes mammography into four levels based on breast density, ranging from ACR-A (least dense) to ACR-D (most dense). Computer-aided diagnostic (CAD) systems can now detect suspicious regions in mammograms and identify abnormalities more quickly and accurately than human readers. However, their performance is still influenced by the tissue density level, which must be considered when designing such systems. In this paper, we propose a novel method that uses CycleGANs to transform suspicious regions of mammograms from ACR-B, -C, and -D levels to ACR-A level. This transformation aims to reduce the masking effect caused by thick tissue and separate cancerous regions from surrounding tissue. Our proposed system enhances the performance of conventional CNN-based classifiers significantly by focusing on regions of interest that would otherwise be misidentified due to fatty masking. Extensive testing on different types of mammograms (digital and scanned X-ray film) demonstrates the effectiveness of our system in identifying normal, benign, and malignant regions of interest.
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来源期刊
CiteScore
3.20
自引率
0.00%
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审稿时长
11 weeks
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