论文标题
具有深度特征的手工制作的特征：用于常规结肠癌组织病理学核图像分类的分析研究
Ensembling Handcrafted Features with Deep Features: An Analytical Study for Classification of Routine Colon Cancer Histopathological Nuclei Images
论文作者
论文摘要
在医学组织病理学图像中，基于深度学习（DL）的方法是最受欢迎的解决方案之一，用于对患病的活检样本进行分类，细分和检测。但是，鉴于由于存在阶层内变异性和异质性而导致的医疗数据集的复杂性质，因此使用复杂DL模型的使用可能不会使最佳性能达到适合于病理学家的水平。因此，具有包含域名的手工制作功能（HC-F）的范围的集合DL方法启发了这项工作。通过实验，我们试图强调，如果没有与相关数据集进行适当的分析，则不能直接将单个DL网络（特定于域或最先进的预训练模型）直接用作基础模型。我们已经使用了F1测量，精度，召回，AUC和横向渗透损失来分析我们的方法的性能。我们从结果观察到DL特征集合会在模型的整体性能方面显着改善，而域不可知的HC-F对DL模型的性能保持休眠状态。
The use of Deep Learning (DL) based methods in medical histopathology images have been one of the most sought after solutions to classify, segment, and detect diseased biopsy samples. However, given the complex nature of medical datasets due to the presence of intra-class variability and heterogeneity, the use of complex DL models might not give the optimal performance up to the level which is suitable for assisting pathologists. Therefore, ensemble DL methods with the scope of including domain agnostic handcrafted Features (HC-F) inspired this work. We have, through experiments, tried to highlight that a single DL network (domain-specific or state of the art pre-trained models) cannot be directly used as the base model without proper analysis with the relevant dataset. We have used F1-measure, Precision, Recall, AUC, and Cross-Entropy Loss to analyse the performance of our approaches. We observed from the results that the DL features ensemble bring a marked improvement in the overall performance of the model, whereas, domain agnostic HC-F remains dormant on the performance of the DL models.
