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BUSI乳腺超声图像数据集

上传者： diidiii | 上传时间： 2025-04-21 11:35:32 | 文件大小： 159.94MB | 文件类型： ZIP
BUSI（Breast Ultrasound Image）是一个包含乳腺超声图像的分类和分割数据集。该数据集包括了 2018 年收集的乳腺超声波图像，涵盖了 25 至 75 岁的 600 名女性患者。数据集由 780 张图像组成，每张图像的平均大小为 500*500 像素。这些图像被划分为三类：正常、良性和恶性。而在良性和恶性乳腺超声图像中，还包含了对应胸部肿瘤的详细分割标注，为深入研究和精准诊断提供了关键信息。这份数据集不仅为乳腺癌研究提供了丰富的图像资源和宝贵支持。 乳腺超声成像技术是一种常用的乳腺疾病检查方法，它通过超声波来获取乳腺组织的图像，具有无创、无痛、操作简便、成本低等特点，是早期发现乳腺病变的重要手段之一。BUSI乳腺超声图像数据集是专门为乳腺病变的分类和分割研究而构建的，对于医疗影像学以及人工智能辅助诊断领域具有重要价值。 数据集中的图像来自2018年的收集，涵盖了广泛年龄段的女性患者，从25岁至75岁不等。由于乳腺疾病的发病与年龄有一定关联，不同年龄段的女性患者可能表现出不同的超声图像特征，这对于研究乳腺病变的年龄分布特征、不同年龄段的发病风险评估等都提供了宝贵的信息。 数据集包含了780张高分辨率的超声图像，每张图像的平均大小为500x500像素，这样的分辨率足以捕捉乳腺组织的细微结构，对于病变区域的辨识和分析至关重要。图像被分为三个主要类别：正常、良性以及恶性。这种分类对于医疗专业人员在临床中进行快速准确的诊断提供了直接帮助，同时也为计算机辅助诊断（CAD）系统的学习与验证提供了基础数据。 在良性与恶性图像中，数据集还包含了详细的肿瘤分割标注，标注区域通常指的是病变的轮廓或边缘，这对于图像分割、计算机视觉识别等任务至关重要。通过这些详细标注，研究人员和工程师可以训练和测试更为精准的图像分割算法，识别和量化肿瘤区域，进而辅助医生在制定治疗方案时做出更为科学的决策。 除了图像本身，该数据集对于深入研究乳腺癌的潜在病理机制、影像学特征与病理诊断之间的联系提供了坚实的数据支撑。医生和科研人员可以利用这些数据进行模式识别、图像分析，以及探索可能存在的影像学标志物，这些标志物可能成为未来诊断乳腺癌的新途径。 此外，BUSI乳腺超声图像数据集还支持跨学科合作，如医学影像学、数据科学和人工智能领域的结合，有助于推动医疗影像分析技术的进步。通过构建和应用深度学习模型，可以实现从传统影像学检查到人工智能辅助诊断的转变，提高乳腺癌的筛查和诊断效率。 BUSI乳腺超声图像数据集不仅为乳腺癌的基础和临床研究提供了丰富的图像资源，也为开发和验证智能化的医学影像分析工具提供了重要的数据支撑，具有较高的应用价值和科研意义。

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