Lesiondiff: enhanced breast cancer classification via dynamic lesion amplification using diffusion models
Breast cancer is a leading cause of mortality among women, underscoring the critical need for accurate and early diagnosis to enhance treatment efficacy. Traditional imaging techniques are limited in their ability to differentiate between benign and malignant lesions, particularly in the early stage...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21681163.2024.2433478 |
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| _version_ | 1846149851286339584 |
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| author | Yinyi Lai Yifan Liu Qiwen Zhang Jiaqi Shang Xinyi Qiu Jun Yan |
| author_facet | Yinyi Lai Yifan Liu Qiwen Zhang Jiaqi Shang Xinyi Qiu Jun Yan |
| author_sort | Yinyi Lai |
| collection | DOAJ |
| description | Breast cancer is a leading cause of mortality among women, underscoring the critical need for accurate and early diagnosis to enhance treatment efficacy. Traditional imaging techniques are limited in their ability to differentiate between benign and malignant lesions, particularly in the early stages, for there are very few images available for the lesion area and the resolution of these images is poor. This paper introduces a novel lesion diffusion model that dynamically amplifies lesion areas, providing a multi-frame analysis to improve classification accuracy. By integrating time-aware motion modeling, the proposed method tracks temporal changes in lesions, Generating a sequence of magnified frames highlighting subtle lesion features. Tested on the BUSI breast ultrasound dataset, our model achieved a 10.269% improvement in classification accuracy over baseline methods, with an average gain of 4.645% across multiple frames. The results demonstrate the model’s ability to enhance the claim and diagnostic utility of breast cancer images after magnification This dynamic lesion amplification approach presents a significant advancement in computer-aided breast cancer diagnostics, offering new possibilities for improving early-stage detection. |
| format | Article |
| id | doaj-art-9aabe0c370b343d89d0e8f182a13e2f2 |
| institution | Kabale University |
| issn | 2168-1163 2168-1171 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
| spelling | doaj-art-9aabe0c370b343d89d0e8f182a13e2f22024-11-29T10:29:56ZengTaylor & Francis GroupComputer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization2168-11632168-11712024-12-0112110.1080/21681163.2024.2433478Lesiondiff: enhanced breast cancer classification via dynamic lesion amplification using diffusion modelsYinyi Lai0Yifan Liu1Qiwen Zhang2Jiaqi Shang3Xinyi Qiu4Jun Yan5Department of Mechanical Engineering, Hohai University, Nanjing, ChinaDepartment of Mechanical Engineering, Hohai University, Nanjing, ChinaDepartment of Mechanical Engineering, Hohai University, Nanjing, ChinaDepartment of Mechanical Engineering, Hohai University, Nanjing, ChinaThe First Affiliated Hospital, Guangdong Pharmaceutical University, Guangzhou, ChinaClinical Pharmacy, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, ChinaBreast cancer is a leading cause of mortality among women, underscoring the critical need for accurate and early diagnosis to enhance treatment efficacy. Traditional imaging techniques are limited in their ability to differentiate between benign and malignant lesions, particularly in the early stages, for there are very few images available for the lesion area and the resolution of these images is poor. This paper introduces a novel lesion diffusion model that dynamically amplifies lesion areas, providing a multi-frame analysis to improve classification accuracy. By integrating time-aware motion modeling, the proposed method tracks temporal changes in lesions, Generating a sequence of magnified frames highlighting subtle lesion features. Tested on the BUSI breast ultrasound dataset, our model achieved a 10.269% improvement in classification accuracy over baseline methods, with an average gain of 4.645% across multiple frames. The results demonstrate the model’s ability to enhance the claim and diagnostic utility of breast cancer images after magnification This dynamic lesion amplification approach presents a significant advancement in computer-aided breast cancer diagnostics, offering new possibilities for improving early-stage detection.https://www.tandfonline.com/doi/10.1080/21681163.2024.2433478Breast cancerlesion diffusion modeldynamic image amplificationmedical imagingmotion modeling |
| spellingShingle | Yinyi Lai Yifan Liu Qiwen Zhang Jiaqi Shang Xinyi Qiu Jun Yan Lesiondiff: enhanced breast cancer classification via dynamic lesion amplification using diffusion models Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization Breast cancer lesion diffusion model dynamic image amplification medical imaging motion modeling |
| title | Lesiondiff: enhanced breast cancer classification via dynamic lesion amplification using diffusion models |
| title_full | Lesiondiff: enhanced breast cancer classification via dynamic lesion amplification using diffusion models |
| title_fullStr | Lesiondiff: enhanced breast cancer classification via dynamic lesion amplification using diffusion models |
| title_full_unstemmed | Lesiondiff: enhanced breast cancer classification via dynamic lesion amplification using diffusion models |
| title_short | Lesiondiff: enhanced breast cancer classification via dynamic lesion amplification using diffusion models |
| title_sort | lesiondiff enhanced breast cancer classification via dynamic lesion amplification using diffusion models |
| topic | Breast cancer lesion diffusion model dynamic image amplification medical imaging motion modeling |
| url | https://www.tandfonline.com/doi/10.1080/21681163.2024.2433478 |
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