Pixel-level labeling of regions of interest in an image is a key step in building a labeled training dataset for supervised deep learning networks of images. However, traditional manual labeling of cancerous regions in digital pathological images by doctors is time-consuming and inefficient. To address this issue, this paper proposes an automatic labeling method for whole slide images, which combines clustering and edge detection techniques. The proposed method utilizes the multi-level feature fusion model and the Long-Short Term Memory network to discriminate the cancerous nature of the whole slide images, thereby improving the classification accuracy of the whole slide images. Subsequently, the automatic labeling of cancerous regions is achieved by integrating a density-based clustering algorithm and an edge point extraction algorithm, both based on the discriminated results of the cancerous properties of whole slide images. The experimental results demonstrate the effectiveness of the proposed method, which offers an efficient and accurate solution to the challenging task of cancerous region labeling in digital pathological images.

中文翻译：

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结合聚类和边缘检测技术的全玻片病理图像自动标注新方法

图像中感兴趣区域的像素级标记是为图像的监督深度学习网络构建标记训练数据集的关键步骤。然而，传统的医生对数字病理图像中癌变区域的手动标记既耗时又低效。为了解决这个问题，本文提出了一种结合聚类和边缘检测技术的整个幻灯片图像的自动标记方法。该方法利用多级特征融合模型和长短期记忆网络来区分整个幻灯片图像的癌性，从而提高整个幻灯片图像的分类精度。随后，通过集成基于密度的聚类算法和边缘点提取算法来实现癌变区域的自动标记，这两种算法都基于整个幻灯片图像癌变特性的判别结果。实验结果证明了该方法的有效性，为数字病理图像中癌性区域标记的挑战性任务提供了高效、准确的解决方案。