Histopathologic Images (HI) are the gold standard for evaluation of some tumors. However, the analysis of such images is challenging even for experienced pathologists, resulting in problems of inter and intra observer. Besides that, the analysis is time and resource consuming. One of the ways to accelerate such an analysis is by using Computer Aided Diagnosis systems. In this work we present a literature review about the computing techniques to process HI, including shallow and deep methods. We cover the most common tasks for processing HI such as segmentation, feature extraction, unsupervised learning and supervised learning. A dataset section show some datasets found during the literature review. We also bring a study case of breast cancer classification using a mix of deep and shallow machine learning methods. The proposed method obtained an accuracy of 91% in the best case, outperforming the compared baseline of the dataset.
翻译:历史病理图象(HI)是评估某些肿瘤的金本位标准。然而,分析这些图象即使对有经验的病理学家来说也具有挑战性,造成观察者之间和内部的问题。此外,分析也是时间和资源消耗。加速这种分析的方法之一是使用计算机辅助诊断系统。在这项工作中,我们介绍了关于处理HI的计算技术的文献审查,包括浅度和深度方法。我们涵盖了处理HI的最常见任务,如分解、特征提取、不受监督的学习和受监督的学习。一个数据集部分展示了在文献审查期间发现的一些数据集。我们还带来了利用深层和浅层机器学习方法混合进行乳腺癌分类的研究案例。拟议方法在最佳情况下获得了91%的准确性,超过了数据集的比较基线。