Abstract In this work, we propose to automate the pre-cancerous tissue abnormality analysis by performing the classification of image patches using a novel two-stage convolutional neural network (CNN) based framework. Rather than training a model with features that may correlate among various classes, we propose to train a model using the features which vary across the different classes. Our framework processes the input image to locate the region of interest (glandular structures) and then feeds the processed image to a classification model for abnormality prediction. Our experiments show that our proposed approach improves the classification performance by up to 7% using CNNs and more than 10% while using texture descriptors. When testing with gland segmented images, our experiments reveal that the performance of our classification approach is dependent on the gland segmentation approach which is a key task in gland structure-guided classification.

中文翻译：

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使用深度学习对显微结直肠图像进行腺体结构引导分类

摘要 在这项工作中，我们建议通过使用基于新的两阶段卷积神经网络 (CNN) 的框架对图像块进行分类来自动化癌前组织异常分析。我们建议使用在不同类别中不同的特征来训练模型，而不是训练具有可能在不同类别之间相关的特征的模型。我们的框架处理输入图像以定位感兴趣的区域（腺体结构），然后将处理后的图像提供给分类模型进行异常预测。我们的实验表明，我们提出的方法在使用 CNN 时将分类性能提高了 7%，在使用纹理描述符时提高了 10% 以上。使用腺体分割图像进行测试时，