Breast cancer has a high mortality rate among cancers. If the type of breast tumor can be correctly diagnosed at an early stage, the survival rate of the patients will be greatly improved. Considering the actual clinical needs, the classification model of breast pathology images needs to have the ability to make a correct classification, even in facing image data with different characteristics. The existing convolutional neural network (CNN)-based models for the classification of breast tumor pathology images lack the requisite generalization capability to maintain high accuracy when confronted with pathology images of varied characteristics. Consequently, this study introduces a new classification model, STMLAN (Single-Task Meta Learning with Auxiliary Network), which integrates Meta Learning and an auxiliary network. Single-Task Meta Learning was proposed to endow the model with generalization ability, and the auxiliary network was used to enhance the feature characteristics of breast pathology images. The experimental results demonstrate that the STMLAN model proposed in this study improves accuracy by at least 1.85% in challenging multi-classification tasks compared to the existing methods. Furthermore, the Silhouette Score corresponding to the features learned by the model has increased by 31.85%, reflecting that the proposed model can learn more discriminative features, and the generalization ability of the overall model is also improved.
乳腺癌在各类癌症中具有较高的死亡率。若能在早期阶段准确诊断乳腺肿瘤类型，患者的生存率将显著提升。考虑到实际临床需求，乳腺病理图像分类模型需具备面对不同特征图像数据时仍能做出正确分类的能力。现有基于卷积神经网络（CNN）的乳腺肿瘤病理图像分类模型在面对不同特征的病理图像时，普遍缺乏维持高准确率所需的泛化能力。为此，本研究提出一种融合元学习与辅助网络的新型分类模型STMLAN（Single-Task Meta Learning with Auxiliary Network）。通过引入单任务元学习赋予模型泛化能力，并利用辅助网络增强乳腺病理图像的特征表达。实验结果表明，在具有挑战性的多分类任务中，本研究提出的STMLAN模型相较于现有方法至少提升了1.85%的准确率。此外，模型所学特征对应的轮廓系数提升了31.85%，表明该模型能够学习更具判别性的特征，整体模型的泛化能力亦得到增强。
Breast Tumor Tissue Image Classification Using Single-Task Meta Learning with Auxiliary Network
肿瘤(癌症)患者之家