鉴于人类生活方式的显着改变，结肠癌的发病率迅速增加。由于结肠癌与其他结肠相关疾病的症状相似，诊断过程常常复杂。为了最大限度减少误诊，临床医学领域的结肠癌诊断基于深度学习的方法取得了显著进展，提供了更精确的检测和改善的患者结果。尽管取得了这些进展，但这些技术的实际应用仍然面临两个主要挑战：1）由于需要专家标注，仅使用有限数量的标签进行诊断；2）存在多种疾病类型，当模型遇到陌生的疾病类别时可能导致误诊。为了克服这些障碍，我们提出了一种整合了通用领域适应（UniDA）的方法。通过优化源域样本的分歧，我们的方法检测噪音。此外，为了识别源域中不存在的类别，我们优化目标域中无标签样本的分歧。对两个胃肠道数据集的实验验证表明，我们的方法在识别未知疾病类别方面超过了当前最先进的领域适应技术。值得注意的是，我们提出的方法是首个用于鉴定未知类别疾病的医学图像诊断工作。版权所有© 2023 Elsevier Ltd. 保留所有权利。

Given the significant changes in human lifestyle, the incidence of colon cancer has rapidly increased. The diagnostic process can often be complicated due to symptom similarities between colon cancer and other colon-related diseases. In an effort to minimize misdiagnosis, deep learning-based approaches for colon cancer diagnosis have notably progressed within the field of clinical medicine, offering more precise detection and improved patient outcomes. Despite these advancements, practical application of these techniques continues to encounter two major challenges: 1) due to the need for expert annotation, only a limited number of labels are utilized for diagnosis; and 2) the existence of diverse disease types can lead to misdiagnosis when the model encounters unfamiliar disease categories. To overcome these hurdles, we present a method incorporating Universal Domain Adaptation (UniDA). By optimizing the divergence of samples in the source domain, our method detects noise. Furthermore, to identify categories that are not present in the source domain, we optimize the divergence of unlabeled samples in the target domain. Experimental validation on two gastrointestinal datasets demonstrates that our method surpasses current state-of-the-art domain adaptation techniques in identifying unknown disease classes. It is worth noting that our proposed method is the first work of medical image diagnosis aimed at the identification of unknown categories of diseases.Copyright © 2023 Elsevier Ltd. All rights reserved.