论文标题
使用深度学习模型的乳腺组织的组织病理学成像分类用于癌症诊断支持
Histopathological Imaging Classification of Breast Tissue for Cancer Diagnosis Support Using Deep Learning Models
论文作者
论文摘要
根据一些医学成像技术，称为苏木精和曙红的乳腺组织病理学图像被认为是癌症诊断的金标准。基于将病理图像（WSI）划分为多个补丁的想法，我们使用了从左至右滑动的窗口[512,512]，从上到下滑动，每个滑动步骤都重叠50％，以增强来自ICIAR 2018 Grand Challenge收集的400张图像的数据集上的数据集。然后使用Effficientnet模型将乳腺癌的组织病理学图像分类为4种类型：正常，良性，癌，侵入性癌。 Effficientnet模型是一个最近开发的模型，该模型均匀地缩放了网络的宽度，深度和分辨率，该模型具有一组固定缩放因子，非常适合具有高分辨率的训练图像。该模型的结果具有相当具有竞争性的分类效率，在训练集上达到98％的精度，评估集的精度为93％。
According to some medical imaging techniques, breast histopathology images called Hematoxylin and Eosin are considered as the gold standard for cancer diagnoses. Based on the idea of dividing the pathologic image (WSI) into multiple patches, we used the window [512,512] sliding from left to right and sliding from top to bottom, each sliding step overlapping by 50% to augmented data on a dataset of 400 images which were gathered from the ICIAR 2018 Grand Challenge. Then use the EffficientNet model to classify and identify the histopathological images of breast cancer into 4 types: Normal, Benign, Carcinoma, Invasive Carcinoma. The EffficientNet model is a recently developed model that uniformly scales the width, depth, and resolution of the network with a set of fixed scaling factors that are well suited for training images with high resolution. And the results of this model give a rather competitive classification efficiency, achieving 98% accuracy on the training set and 93% on the evaluation set.
