Department of Computer Science and Engineering, University of Louisville, Louisville, KY, 40292, USA.
eVida Research Group, University of Deusto, Bilbao, 4800, Spain.
Comput Methods Programs Biomed. 2022 Jun;221:106884. doi: 10.1016/j.cmpb.2022.106884. Epub 2022 May 13.
Computer-aided-detection (CAD) systems have been developed to assist radiologists on finding suspicious lesions in mammogram. Deep Learning technology have recently succeeded to increase the chance of recognizing abnormality at an early stage in order to avoid unnecessary biopsies and decrease the mortality rate. In this study, we investigated the effectiveness of an end-to-end fusion model based on You-Only-Look-Once (YOLO) architecture, to simultaneously detect and classify suspicious breast lesions on digital mammograms. Four categories of cases were included: Mass, Calcification, Architectural Distortions, and Normal from a private digital mammographic database including 413 cases. For all cases, Prior mammograms (typically scanned 1 year before) were all reported as Normal, while Current mammograms were diagnosed as cancerous (confirmed by biopsies) or healthy.
We propose to apply the YOLO-based fusion model to the Current mammograms for breast lesions detection and classification. Then apply the same model retrospectively to synthetic mammograms for an early cancer prediction, where the synthetic mammograms were generated from the Prior mammograms by using the image-to-image translation models, CycleGAN and Pix2Pix.
Evaluation results showed that our methodology could significantly detect and classify breast lesions on Current mammograms with a highest rate of 93% ± 0.118 for Mass lesions, 88% ± 0.09 for Calcification lesions, and 95% ± 0.06 for Architectural Distortion lesions. In addition, we reported evaluation results on Prior mammograms with a highest rate of 36% ± 0.01 for Mass lesions, 14% ± 0.01 for Calcification lesions, and 50% ± 0.02 for Architectural Distortion lesions. Normal mammograms were accordingly classified with an accuracy rate of 92% ± 0.09 and 90% ± 0.06 respectively on Current and Prior exams.
Our proposed framework was first developed to help detecting and identifying suspicious breast lesions in X-ray mammograms on their Current screening. The work was also suggested to reduce the temporal changes between pairs of Prior and follow-up screenings for early predicting the location and type of abnormalities in Prior mammogram screening. The paper presented a CAD method to assist doctors and experts to identify the risk of breast cancer presence. Overall, the proposed CAD method incorporates the advances of image processing, deep learning and image-to-image translation for a biomedical application.
计算机辅助检测（CAD）系统旨在帮助放射科医生发现乳房 X 光片中的可疑病变。最近，深度学习技术成功提高了早期识别异常的机会，以避免不必要的活检并降低死亡率。在这项研究中，我们研究了基于端到端融合模型的有效性，该模型基于单次观察（YOLO）架构，可同时检测和分类数字乳房 X 光片中的可疑乳房病变。该研究纳入了来自一家私人数字乳房 X 光数据库的 413 例病例，包括肿块、钙化、结构扭曲和正常四种类型。所有病例的前次乳房 X 光片（通常在 1 年前扫描）均报告为正常，而当前的乳房 X 光片则被诊断为癌症（通过活检证实）或健康。
我们提出将基于 YOLO 的融合模型应用于当前的乳房 X 光片中进行乳房病变检测和分类。然后，将相同的模型回溯应用于合成的乳房 X 光片中，以进行早期癌症预测，其中合成的乳房 X 光片是通过图像到图像翻译模型（CycleGAN 和 Pix2Pix）从前次的乳房 X 光片中生成的。
评估结果表明，我们的方法可以显著检测和分类当前乳房 X 光片中的乳房病变，肿块病变的最高检出率为 93%±0.118，钙化病变的最高检出率为 88%±0.09，结构扭曲病变的最高检出率为 95%±0.06。此外，我们还报告了前次乳房 X 光片的评估结果，肿块病变的最高检出率为 36%±0.01，钙化病变的最高检出率为 14%±0.01，结构扭曲病变的最高检出率为 50%±0.02。正常的乳房 X 光片在当前和前次检查中的分类准确率分别为 92%±0.09 和 90%±0.06。
本研究首次开发了一种框架，旨在帮助检测和识别 X 射线乳房 X 光片中的可疑乳房病变。该研究还建议减少前次和随访筛查之间的时间变化，以便早期预测前次筛查中异常的位置和类型。本文提出了一种 CAD 方法，以帮助医生和专家识别乳腺癌存在的风险。总体而言，该 CAD 方法融合了图像处理、深度学习和图像到图像翻译等领域的先进技术，应用于生物医学领域。
