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Abstract：
Early detection of the adenocarcinoma cancer in colon tissue by means of explainable deep learning, by classifying histological images and providing visual explainability on model prediction. Considering that in recent years, deep learning techniques have emerged as powerful techniques in medical image analysis, offering unprecedented accuracy and efficiency, in this paper we propose a method to automatically detect the presence of cancerous cells in colon tissue images. Various deep learning architectures are considered, with the aim of considering the best one in terms of quantitative and qualitative results. As a matter of fact, we consider qualitative results by taking into account the so-called prediction explainability, by providing a way to highlight on the tissue images the areas that from the model point of view are related to the presence of colon cancer. The experimental analysis, performed on 10,000 colon issue images, showed the effectiveness of the proposed method by obtaining an accuracy equal to 0.99. The experimental analysis shows that the proposed method can be successfully exploited for colon cancer detection and localisation from tissue images.
摘要：
通过可解释的深度学习在结肠组织中早期发现腺癌，通过对组织学图像进行分类，并在模型预测上提供视觉可解释性。考虑到近年来，深度学习技术已经成为医学图像分析中的强大技术，提供前所未有的准确性和效率，在本文中，我们提出了一种自动检测结肠组织图像中癌细胞存在的方法。考虑了各种深度学习架构，目的是在定量和定性结果方面考虑最佳结果。事实上,我们通过考虑所谓的预测可解释性来考虑定性结果，通过提供一种在组织图像上突出显示从模型角度来看与结肠癌存在相关的区域的方法。实验分析,在10,000个结肠问题图像上执行，通过获得等于0.99的精度，证明了该方法的有效性。实验分析表明，所提出的方法可以成功地用于从组织图像中检测和定位结肠癌。
