Abstract
Given the emergence of deep learning, digital pathology has gained popularity for cancer diagnosis based on histology images. Deep weakly supervised object localization (WSOL) models can be trained to classify histology images according to cancer grade and identify regions of interest (ROIs) for interpretation, using inexpensive global image-class annotations. A WSOL model initially trained on some labeled source image data can be adapted using unlabeled target data in cases of significant domain shifts caused by variations in staining, scanners, and cancer type. In this paper, we focus on source-free (unsupervised) domain adaptation (SFDA), a challenging problem where a pre-trained source model is adapted to a new target domain without using any source domain data for privacy and efficiency reasons. SFDA of WSOL models raises several challenges in histology, most notably because they are not intended to adapt for both classification and localization tasks. In this paper, 4 state-of-the-art SFDA methods, each one representative of a main SFDA family, are compared for WSOL in terms of classification and localization accuracy. They are the SFDA-Distribution Estimation, Source HypOthesis Transfer, Cross-Domain Contrastive Learning, and Adaptively Domain Statistics Alignment. Experimental results on the challenging Glas (smaller, breast cancer) and Camelyon16 (larger, colon cancer) histology datasets indicate that these SFDA methods typically perform poorly for localization after adaptation when optimized for classification.
Abstract (translated)
鉴于深度学习的出现，基于组织图像的癌症诊断在病理学图像中得到了广泛应用。可以训练深度弱监督物体定位（WSOL）模型根据癌症分期对组织图像进行分类，并识别感兴趣区域（ROIs）用于解释。使用廉价的全球图像类注释可以帮助训练WSOL模型。在本文中，我们关注源免费（无监督）领域适应（SFDA）问题，这是一个具有挑战性的问题， 在这种问题中，预训练的源模型被适应到新的目标领域，而不会使用任何源域数据，出于隐私和效率原因。 SFDA的WSOL模型在组织学中引起了几个挑战，尤其是因为他们不是为了适应分类和定位任务而设计的。在本文中，我们比较了四个最先进的SFDA方法，每个都是一些主要SFDA家族的代表，在WSOL方面的分类和定位准确性。它们是SFDA-分布估计、源假设转移、跨领域对比学习以及自适应领域统计对齐。在具有挑战性的Glas（较小，乳腺癌）和Camelyon16（较大，结肠癌）组织数据集的实验结果中，这些SFDA方法在优化分类时通常表现不佳。
URL
https://arxiv.org/abs/2404.19113
