Laboratoire SIMPA, Département d'Informatique, Université des Sciences et de la Technologie d'Oran Mohamed Boudiaf (USTO-MB), Oran 31000, Algeria.
Centre de Recherche en Informatique de Lens, CRIL, CNRS, Université d'Artois, 62307 Lens, France.
Sensors (Basel). 2023 Aug 22;23(17):7318. doi: 10.3390/s23177318.
Hospitals generate a significant amount of medical data every day, which constitute a very rich database for research. Today, this database is still not exploitable because to make its valorization possible, the images require an annotation which remains a costly and difficult task. Thus, the use of an unsupervised segmentation method could facilitate the process. In this article, we propose two approaches for the semantic segmentation of breast cancer histopathology images. On the one hand, an autoencoder architecture for unsupervised segmentation is proposed, and on the other hand, an improvement U-Net architecture for supervised segmentation is proposed. We evaluate these models on a public dataset of histological images of breast cancer. In addition, the performance of our segmentation methods is measured using several evaluation metrics such as accuracy, recall, precision and F1 score. The results are competitive with those of other modern methods.
医院每天产生大量的医疗数据，这些数据构成了一个非常丰富的研究数据库。如今，这个数据库仍然无法被利用，因为要使其具有价值，这些图像需要进行注释，而注释仍然是一项昂贵且困难的任务。因此，使用无监督分割方法可以简化这个过程。在本文中，我们提出了两种用于乳腺癌组织病理学图像语义分割的方法。一方面，我们提出了一种用于无监督分割的自动编码器架构，另一方面，我们提出了一种用于监督分割的 U-Net 架构的改进方法。我们在一个公共的乳腺癌组织学图像数据集上评估了这些模型。此外，我们使用多种评估指标（如准确性、召回率、精度和 F1 分数）来衡量我们分割方法的性能。我们的结果与其他现代方法的结果相当。
