Computer Vision - ECCV 2022 Workshops - Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part III
Computer Vision - ECCV 2022 Workshops - Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part III
复制标题
计算机视觉 - ECCV 2022 研讨会 - 以色列特拉维夫，2022 年 10 月 23-27 日，会议记录，第三部分
DOI：
10.1007/978-3-031-25066-8_31
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Vuong T
中科院分区：
文献类型：
--
作者：
Vuong T
组织病理学图像的外观取决于组织类型、染色和数字化程序。这些因源而异，是域转移问题的潜在原因。由于这个问题，尽管深度学习模型在计算病理学中取得了巨大的成功，但当我们将其应用于另一个领域时，在特定领域训练的模型仍然可能表现不佳。为了克服这个问题，我们提出了一种新的增强方法PatchShuffling和一种新的自监督对比学习框架IMPaSh，用于预训练深度学习模型。使用这些，我们得到了一个ResNet50编码器，可以提取抗域移位的图像表示。我们比较了我们的衍生表示对其他领域的泛化技术的基础上获得的结直肠组织图像的跨域分类。我们表明，所提出的方法优于其他传统的组织学领域的适应和国家的最先进的自监督学习方法。代码可从以下网址获得：https://github.com/trinhvg/IMPash.
The appearance of histopathology images depends on tissue type, staining and digitization procedure. These vary from source to source and are the potential causes for domain-shift problems. Owing to this problem, despite the great success of deep learning models in computational pathology, a model trained on a specific domain may still perform sub-optimally when we apply them to another domain. To overcome this, we propose a new augmentation called PatchShuffling and a novel self-supervised contrastive learning framework named IMPaSh for pre-training deep learning models. Using these, we obtained a ResNet50 encoder that can extract image representation resistant to domain-shift. We compared our derived representation against those acquired based on other domain-generalization techniques by using them for the cross-domain classification of colorectal tissue images. We show that the proposed method outperforms other traditional histology domain-adaptation and state-of-the-art self-supervised learning methods. Code is available at: https://github.com/trinhvg/IMPash.