AISSLab Breast Cancer Dataset: Toward General AI Harmonization with Real Mammogram Imaging
收藏doi.org2025-03-23 收录
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http://doi.org/10.17632/zp8yfhvndv.1
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资源简介：
The AISSLab Breast Cancer Dataset is a collection of mammogram images by experts from the Ma'amon's Diagnostic Centre Mammogram Images for Breast Cancer (MDCMI-BC) in Yemen. It is designed to support advancements in breast cancer research and computer-aided diagnosis (CAD) systems. To facilitate research in breast cancer detection, focusing on harmonizing AI with diverse imaging data. This dataset emphasizes improving diagnostic accuracy and is available for academic and clinical research applications.
If you are using this dataset for research purpose kindly cite the following papers:
[1] A. M. Al-Hejri, R. M. Al-Tam, M. Fazea, A. H. Sable, S. Lee, and M. A. Al-antari, “ETECADx: Ensemble Self-Attention Transformer Encoder for Breast Cancer Diagnosis Using Full-Field Digital X-ray Breast Images,” Diagnostics, vol. 13, no. 1, p. 89, Dec. 2022, doi: 10.3390/diagnostics13010089.
[2] R. M. Al-Tam, A. M. Al-Hejri, S. S. Alshamrani, M. A. Al-antari, and S. M. Narangale, “Multimodal breast cancer hybrid explainable computer-aided diagnosis using medical mammograms and ultrasound Images,” Biocybern. Biomed. Eng., vol. 44, no. 3, pp. 731–758, Jul. 2024, doi: 10.1016/j.bbe.2024.08.007.
AISSLab 乳腺癌数据集系由也门马阿蒙诊断中心乳腺摄影图像乳腺摄影图像（MDCMI-BC）的专家所收集的一组乳腺摄影图像。该数据集旨在支持乳腺癌研究和计算机辅助诊断（CAD）系统的进步，致力于促进人工智能与多样化成像数据的协调。本数据集强调提升诊断准确性，并可供学术和临床研究应用。在使用此数据集进行科研目的时，请引用以下论文：
[1] A. M. Al-Hejri, R. M. Al-Tam, M. Fazea, A. H. Sable, S. Lee, and M. A. Al-antari, “ETECADx: Ensemble Self-Attention Transformer Encoder for Breast Cancer Diagnosis Using Full-Field Digital X-ray Breast Images,” Diagnostics, vol. 13, no. 1, p. 89, Dec. 2022, doi: 10.3390/diagnostics13010089.
[2] R. M. Al-Tam, A. M. Al-Hejri, S. S. Alshamrani, M. A. Al-antari, and S. M. Narangale, “Multimodal breast cancer hybrid explainable computer-aided diagnosis using medical mammograms and ultrasound Images,” Biocybern. Biomed. Eng., vol. 44, no. 3, pp. 731–758, Jul. 2024, doi: 10.1016/j.bbe.2024.08.007.
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Mendeley Data
