School of Electrical Engineering, Korea University, Seoul, Republic of Korea.
Department of Computer Science and Engineering, Sejong University, Seoul, Republic of Korea.
Comput Methods Programs Biomed. 2023 Nov;241:107749. doi: 10.1016/j.cmpb.2023.107749. Epub 2023 Aug 9.
Cancer grading in pathology image analysis is a major task due to its importance in patient care, treatment, and management. The recent developments in artificial neural networks for computational pathology have demonstrated great potential to improve the accuracy and quality of cancer diagnosis. These improvements are generally ascribable to the advance in the architecture of the networks, often leading to increase in the computation and resources. In this work, we propose an efficient convolutional neural network that is designed to conduct multi-class cancer classification in an accurate and robust manner via metric learning.
We propose a centroid-aware metric learning network for an improved cancer grading in pathology images. The proposed network utilizes centroids of different classes within the feature embedding space to optimize the relative distances between pathology images, which manifest the innate similarities/dissimilarities between them. For improved optimization, we introduce a new loss function and a training strategy that are tailored to the proposed network and metric learning.
We evaluated the proposed approach on multiple datasets of colorectal and gastric cancers. For the colorectal cancer, two different datasets were employed that were collected from different acquisition settings. the proposed method achieved an accuracy, F1-score, quadratic weighted kappa of 88.7%, 0.849, and 0.946 for the first dataset and 83.3%, 0.764, and 0.907 for the second dataset, respectively. For the gastric cancer, the proposed method obtained an accuracy of 85.9%, F1-score of 0.793, and quadratic weighted kappa of 0.939. We also found that the proposed method outperforms other competing models and is computationally efficient.
The experimental results demonstrate that the prediction results by the proposed network are both accurate and reliable. The proposed network not only outperformed other related methods in cancer classification but also achieved superior computational efficiency during training and inference. The future study will entail further development of the proposed method and the application of the method to other problems and domains.
癌症分级在病理图像分析中是一项重要任务，因为它对患者护理、治疗和管理至关重要。近年来，计算病理学中的人工神经网络在提高癌症诊断的准确性和质量方面表现出了巨大的潜力。这些改进通常归因于网络架构的进步，通常会导致计算和资源的增加。在这项工作中，我们提出了一种高效的卷积神经网络，旨在通过度量学习准确而稳健地进行多类癌症分类。
我们提出了一种基于质心感知的度量学习网络，用于改进病理图像中的癌症分级。所提出的网络利用特征嵌入空间中不同类别的质心来优化病理图像之间的相对距离，这些距离体现了它们之间的固有相似性/差异性。为了进行改进优化，我们引入了一种新的损失函数和训练策略，这些函数和策略专门针对所提出的网络和度量学习进行了定制。
我们在多个结直肠癌和胃癌数据集上评估了所提出的方法。对于结直肠癌，我们使用了来自不同采集设置的两个不同数据集。所提出的方法在第一个数据集上的准确率、F1 分数和二次加权 Kappa 分别为 88.7%、0.849 和 0.946，在第二个数据集上的准确率、F1 分数和二次加权 Kappa 分别为 83.3%、0.764 和 0.907。对于胃癌，所提出的方法的准确率为 85.9%，F1 分数为 0.793，二次加权 Kappa 为 0.939。我们还发现，所提出的方法优于其他竞争模型，并且计算效率高。
实验结果表明，所提出的网络的预测结果既准确又可靠。所提出的网络不仅在癌症分类方面优于其他相关方法，而且在训练和推理过程中还实现了卓越的计算效率。未来的研究将进一步开发所提出的方法，并将该方法应用于其他问题和领域。
