利用卷积神经网络进行组织病理学图像分类以检测淋巴结转移性乳腺癌

Diego Alberto Cadillo-Laurentt, Ernesto Paiva-Peredo
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引用次数: 0

摘要

乳腺癌是目前全球诊断率最高的肿瘤疾病之一,每年新增病例数以千计。早期发现并确定其进展是降低死亡率的关键。为了确定疾病在患者全身的扩散程度,一项经常性检查是对乳房附近的前哨淋巴结进行组织学分析。虽然这项工作由病理专家完成,但通常是一项耗时耗力的工作,而且极有可能出错。本研究提出了一种利用卷积神经网络通过前哨淋巴结组织学成像检测乳腺癌转移的方法。本研究测试并比较了 DenseNet-121、DenseNet-169 和 DenseNet-201 三种模型的性能。实验结果表明,DenseNet-201 的准确度、精确度、灵敏度和特异性(分别为 97.93%、97.4%、97.48% 和 98.24%)可以减少病理学家在诊断过程中的错误,或作为第二意见工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Histopathological Image Classification Using Convolutional Neural Networks for Detection of Metastatic Breast Cancer in Lymph Nodes
Breast cancer is currently one of the most diagnosed oncological diseases worldwide, with thousands of new cases per year. Early detection and identifying its progression are key to overcoming the mortality rate. A recurrent test, to determine how far the disease has spread throughout the patient’s body, is the histological analysis of the sentinel lymph node near the breast. Although an expert pathologist performs this, it is usually an exhausting and time-consuming task, with a high possibility of error. This work presents a method to detect breast cancer metastasis through histological imaging of sentinel lymph nodes using convolutional neural networks. In this study, the performance of three models DenseNet-121, DenseNet-169 and DenseNet-201 are tested and compared. Experimental results indicated that the accuracy, precision, sensitivity and specificity (97.93%, 97.4%, 97.48% and 98.24%) of DenseNet-201 could reduce pathologist errors during the diagnostic process or serve as a second opinion tool.
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