PDF(Pubmed)
Abstract：
Accurate surgical pathological assessment of breast biopsies is essential to the proper management of breast lesions. Identifying histological features, such as nuclear pleomorphism, increased mitotic activity, cellular atypia, patterns of architectural disruption, as well as invasion through basement membranes into surrounding stroma and normal structures, including invasion of vascular and lymphatic spaces, help to classify lesions as malignant. This visual assessment is repeated on numerous slides taken at various sections through the resected tumor, each at different magnifications. Computer vision models have been proposed to assist human pathologists in classification tasks such as these. Using MobileNetV3, a convolutional architecture designed to achieve high accuracy with a compact parameter footprint, we attempted to classify breast cancer images in the BreakHis_v1 breast pathology dataset to determine the performance of this model out-of-the-box. Using transfer learning to take advantage of ImageNet embeddings without special feature extraction, we were able to correctly classify histopathology images broadly as benign or malignant with 0.98 precision, 0.97 recall, and an F1 score of 0.98. The ability to classify into histological subcategories was varied, with the greatest success being with classifying ductal carcinoma (accuracy 0.95), and the lowest success being with lobular carcinoma (accuracy 0.59). Multiclass ROC assessment of performance as a multiclass classifier yielded AUC values ≥0.97 in both benign and malignant subsets. In comparison with previous efforts, using older and larger convolutional network architectures with feature extraction pre-processing, our work highlights that modern, resource-efficient architectures can classify histopathological images with accuracy that at least matches that of previous efforts, without the need for labor-intensive feature extraction protocols. Suggestions to further refine the model are discussed.
摘要：
准确的乳腺活检手术病理评估对于正确管理乳腺病变至关重要。识别组织学特征，比如核多态性，有丝分裂活性增加，细胞异型性,建筑破坏的模式，以及通过基底膜侵入周围基质和正常结构，包括侵犯血管和淋巴空间，有助于将病变分类为恶性。在通过切除的肿瘤的不同切片上拍摄的大量载玻片上重复这种视觉评估，每个在不同的放大倍数。已经提出了计算机视觉模型来帮助人类病理学家进行诸如这些的分类任务。使用MobileNetV3，这是一种卷积体系结构，旨在以紧凑的参数占用空间实现高精度，我们尝试对BreakHis_v1乳腺病理学数据集中的乳腺癌图像进行分类,以确定该模型的性能。使用迁移学习来利用ImageNet嵌入，而无需特殊的特征提取，我们能够正确地将组织病理学图像大致分类为良性或恶性，精度为0.98，0.97召回，F1得分为0.98。分类为组织学亚类的能力各不相同，最大的成功是对导管癌进行分类（准确率0.95），成功率最低的是小叶癌（准确度0.59）。作为多类分类器的多类ROC性能评估在良性和恶性亚群中均产生≥0.97的AUC值。与以前的努力相比，使用具有特征提取预处理的较旧和较大的卷积网络体系结构，我们的工作强调了现代，资源高效的体系结构可以对组织病理学图像进行分类，其准确性至少与以前的工作相匹配，无需劳动密集型特征提取协议。讨论了进一步完善模型的建议。
