文献类型：期刊文献

中文题名：自适应小数据集乳腺癌病理组织分类研究

英文题名：Research on Classification of Breast Cancer Pathological Tissues with Adaptive Small Data Set

作者：和青芳[1];王慧[1];程光[1]

机构：[1]北京联合大学计算机技术研究所,北京100101

第一机构：北京联合大学工科综合实验教学示范中心

年份：2021

卷号：48

期号：S01

起止页码：67-73

中文期刊名：计算机科学

外文期刊名：Computer Science

收录：CSTPCD;;北大核心:【北大核心2020】；CSCD:【CSCD_E2021_2022】；

基金：北京市自然科学基金(L191006);北京联合大学科研项目(XP202021)。

语种：中文

中文关键词：乳腺癌病理组织图像;自适应小数据集;弱特征;卷积神经网络;深度可分离卷积;深度学习;

外文关键词：Breast cancer pathological tissue images;Adaptive small data sets;Weak features;Convolutional neural networks;Deep separable convolution;Deep learning;

摘要：针对乳腺癌病理组织图像数据普遍存在数据集规模小、良性和恶性样本数量分布不均衡、自动识别精度低的现状,利用深度可分离卷积、小卷积核堆叠、增深降维等技术,结合文中提出的"SoftMax+WF"设计具备合理深度和宽度、适应小数据集、轻型的病理组织图像分类模型。在图像旋转、扭曲等传统增强数据方法基础上,采用随机不重复裁切法均衡良、恶性样本数量并扩充数据集。针对训练集中难以聚类的样本,提出"弱特征"概念、"弱特征"样本提取算法和自适应调整、二次训练算法改进模型训练。在参数设置和运行环境相同的条件下,进行8组比对实验,模型的准确率、敏感度、特异度均可达97%以上。实验结果证明文中设计的模型性能稳定,对小数据集和不均衡数据集具有较好的包容性和适应性。
Aiming at the problems of small data set,uneven distribution of benign and malignant samples,and low automatic re-cognition accuracy of breast cancer pathological tissue image data,a lightweight pathological tissue image classification model with reasonable depth and width is designed,which is suitable for small data sets.Based on the traditional data enhancement methods such as image rotation and distortion,the random non-repeated cutting method is used to balance the number of benign and malignant samples and expand the data set.For the samples that are difficult to cluster in the training set,the concept of"weak feature","weak feature"sample extraction algorithm and adaptive adjustment,secondary training algorithm are proposed to improve the model training.Under the condition of the same parameter setting and running environment,eight groups of comparative experiments are carried out,and the accuracy,sensitivity and specificity of the model can reach more than 97%.The experimental results show that the performance of the model designed in this paper is stable,and it has good tolerance and adaptability for small data sets and unbalanced data sets.