## 1 Introduction
However, in medical imaging, obtaining labeled source domain data for transfer learning is often challenging due to privacy concerns and data management regulations. Because of this hardship, efficiently using available pre-trained models through source-free approaches like our UCDA is a desirable alternative. Because medical image data largely originates from hospital databases, there are limitations in openly sharing the datasets used, which can pose challenges for certain high-performance methods. However, an alternative solution, known as source-free domain adaptation, has emerged by capitalizing on the use of pre-trained models rather than directly accessing the sensitive source data and the Uncertainty-guided Asymmetric Consistency Domain Adaptation (UCDA) method that we propose further builds on this framework, augmenting it with innovation that explicitly accounts for inconsistencies and missed representations inherent in previous approaches. Several prior studies have demonstrated the efficacy of source-free domain adaptation (SFDA) in medical image processing [13; 14; 15]. Specifically, these investigations have primarily focused on medical image segmentation and have yielded promising experimental outcomes, demonstrating the potential of source-free domain adaptation in medical image analysis. However, there is a dearth of research regarding the application of source-free methods in medical image classification, predominantly due to the pronounced uncertainties associated with disease representations in medical images when compared to natural image classification tasks. Moreover, existing methods fail to account for the issue of weak model generalizability resulting from data inconsistency across devices and other factors [16]. Additionally, it is necessary to address the problem of decreasing pseudo-label quality caused by network uncertainty stemming from source models. To solve these obstacles, this work enables knowledge transfer between domains while respecting data privacy and confidentiality restrictions, ultimately circumventing the need for sharing the actual datasets. Through source-free domain adaptation, we can leverage the learned representations and patterns encoded within pre-trained source models to effectively analyze target domains without compromising data security or violating legal and ethical considerations. In detail, our study presents Uncertainty-guided Asymmetric Consistency Domain Adaptation (UCDA), a novel method enhancing medical image classification without source data access. UCDA integrates Uncertainty-guided Source-free Transfer Learning (USTL) and Asymmetric Consistency Learning (ACL). USTL ensures secure knowledge transfer, preserving data privacy and effectively addressing network uncertainties, while ACL extracts asymmetric features to align the source and target domain feature spaces (Figure 1). This approach significantly improves classification accuracy and robustness, demonstrating UCDA's potential in advancing medical image analysis with remarkable performance across diverse datasets. In summary, this study presents the following contributions:  
1. In our research, we present an innovative, source-free transfer learning methodology referred to as Uncertainty-guided Asymmetric Consistency Domain Adaptation (UCDA) designed for the classification of histopathological images. UCDA sets itself apart from typical methods by eliminating the requirement for original source data, therefore maintaining data confidentiality in clinical medical situations. The adaptation of the domain is realized via the distillation of knowledge from a pre-existing source model. 2. To tackle the uncertainty issues prevalent in medical image classification, we unveil a source-free knowledge distillation mechanism called Uncertainty-guided Source-free Transfer Learning (USTL). This mechanism employs a strategy guided by uncertainty to aid in the correlation of features between the source model and the target data, thereby augmenting the efficiency of knowledge transfer. 3.