PDF(Pubmed)
Abstract：
Colorectal cancer is a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is a colonoscopy. However, not all colon polyps have the risk of becoming cancerous. Therefore, polyps are classified using different classification systems. After the classification, further treatment and procedures are based on the classification of the polyp. Nevertheless, classification is not easy. Therefore, we suggest two novel automated classifications system assisting gastroenterologists in classifying polyps based on the NICE and Paris classification.
We build two classification systems. One is classifying polyps based on their shape (Paris). The other classifies polyps based on their texture and surface patterns (NICE). A two-step process for the Paris classification is introduced: First, detecting and cropping the polyp on the image, and secondly, classifying the polyp based on the cropped area with a transformer network. For the NICE classification, we design a few-shot learning algorithm based on the Deep Metric Learning approach. The algorithm creates an embedding space for polyps, which allows classification from a few examples to account for the data scarcity of NICE annotated images in our database.
For the Paris classification, we achieve an accuracy of 89.35 %, surpassing all papers in the literature and establishing a new state-of-the-art and baseline accuracy for other publications on a public data set. For the NICE classification, we achieve a competitive accuracy of 81.13 % and demonstrate thereby the viability of the few-shot learning paradigm in polyp classification in data-scarce environments. Additionally, we show different ablations of the algorithms. Finally, we further elaborate on the explainability of the system by showing heat maps of the neural network explaining neural activations.
Overall we introduce two polyp classification systems to assist gastroenterologists. We achieve state-of-the-art performance in the Paris classification and demonstrate the viability of the few-shot learning paradigm in the NICE classification, addressing the prevalent data scarcity issues faced in medical machine learning.
We build two classification systems. One is classifying polyps based on their shape (Paris). The other classifies polyps based on their texture and surface patterns (NICE). A two-step process for the Paris classification is introduced: First, detecting and cropping the polyp on the image, and secondly, classifying the polyp based on the cropped area with a transformer network. For the NICE classification, we design a few-shot learning algorithm based on the Deep Metric Learning approach. The algorithm creates an embedding space for polyps, which allows classification from a few examples to account for the data scarcity of NICE annotated images in our database.
For the Paris classification, we achieve an accuracy of 89.35 %, surpassing all papers in the literature and establishing a new state-of-the-art and baseline accuracy for other publications on a public data set. For the NICE classification, we achieve a competitive accuracy of 81.13 % and demonstrate thereby the viability of the few-shot learning paradigm in polyp classification in data-scarce environments. Additionally, we show different ablations of the algorithms. Finally, we further elaborate on the explainability of the system by showing heat maps of the neural network explaining neural activations.
Overall we introduce two polyp classification systems to assist gastroenterologists. We achieve state-of-the-art performance in the Paris classification and demonstrate the viability of the few-shot learning paradigm in the NICE classification, addressing the prevalent data scarcity issues faced in medical machine learning.
摘要：
背景：结直肠癌是全球癌症相关死亡的主要原因。预防CRC的最佳方法是结肠镜检查。然而,并非所有结肠息肉都有癌变的风险。因此,息肉使用不同的分类系统进行分类。分类后,进一步的治疗和程序是基于息肉的分类。然而，分类并不容易。因此,我们建议使用两种新型自动分类系统,帮助胃肠病学家根据NICE和Paris分类对息肉进行分类.
方法：我们建立了两个分类系统。一种是根据息肉的形状对息肉进行分类（巴黎）。另一种是根据息肉的纹理和表面图案（NICE）对息肉进行分类。介绍了巴黎分类的两步过程：首先，检测和裁剪图像上的息肉，其次,用变压器网络根据裁剪区域对息肉进行分类。对于NICE分类，我们设计了一种基于深度度量学习方法的少射学习算法。该算法为息肉创建了一个嵌入空间，它允许从几个例子中进行分类，以说明我们数据库中NICE注释图像的数据稀缺性。
结果：对于巴黎分类，我们达到了89.35%的准确率，超越了文献中的所有论文，并为公共数据集上的其他出版物建立了新的最新技术和基线准确性。对于NICE分类，我们获得了81.13%的竞争准确率,从而证明了在数据稀缺的环境中,少射学习范式在息肉分类中的可行性.此外,我们展示了算法的不同烧蚀。最后,通过显示解释神经激活的神经网络的热图，我们进一步阐述了系统的可解释性。
结论：总的来说，我们介绍了两种息肉分类系统来帮助胃肠病学家。我们在巴黎分类中实现了最先进的性能，并在NICE分类中展示了少射学习范式的可行性，解决医疗机器学习中面临的普遍数据稀缺问题。
方法：我们建立了两个分类系统。一种是根据息肉的形状对息肉进行分类（巴黎）。另一种是根据息肉的纹理和表面图案（NICE）对息肉进行分类。介绍了巴黎分类的两步过程：首先，检测和裁剪图像上的息肉，其次,用变压器网络根据裁剪区域对息肉进行分类。对于NICE分类，我们设计了一种基于深度度量学习方法的少射学习算法。该算法为息肉创建了一个嵌入空间，它允许从几个例子中进行分类，以说明我们数据库中NICE注释图像的数据稀缺性。
结果：对于巴黎分类，我们达到了89.35%的准确率，超越了文献中的所有论文，并为公共数据集上的其他出版物建立了新的最新技术和基线准确性。对于NICE分类，我们获得了81.13%的竞争准确率,从而证明了在数据稀缺的环境中,少射学习范式在息肉分类中的可行性.此外,我们展示了算法的不同烧蚀。最后,通过显示解释神经激活的神经网络的热图，我们进一步阐述了系统的可解释性。
结论：总的来说，我们介绍了两种息肉分类系统来帮助胃肠病学家。我们在巴黎分类中实现了最先进的性能，并在NICE分类中展示了少射学习范式的可行性，解决医疗机器学习中面临的普遍数据稀缺问题。
