Accurate segmentation of tissue in histopathological images can be very beneficial for defining regions of interest (ROI) for streamline of diagnostic and prognostic tasks. Still, adapting to different domains is essential for histopathology image analysis, as the visual characteristics of tissues can vary significantly across datasets. Yet, acquiring sufficient annotated data in the medical domain is cumbersome and time-consuming. The labeling effort can be significantly reduced by leveraging active learning, which enables the selective annotation of the most informative samples. Our proposed method allows for fine-tuning a pre-trained deep neural network using a small set of labeled data from the target domain, while also actively selecting the most informative samples to label next. We demonstrate that our approach performs with significantly fewer labeled samples compared to traditional supervised learning approaches for similar F1-scores, using barely a 59\% of the training set. We also investigate the distribution of class balance to establish annotation guidelines.

翻译：组织病理图像中的组织精确分割对于定义感兴趣区域（ROI）以简化诊断和预后任务非常有益。然而，由于不同数据集的视觉特征可能存在显著差异，域自适应对于组织病理学图像分析至关重要。尽管如此，在医学领域中获取充足的标注数据既繁琐又耗时。通过利用主动学习，可以选择性标注信息量最大的样本，从而显著减少标注工作量。我们提出的方法能够使用目标域中的少量标注数据对预训练的深度神经网络进行微调，同时主动选择信息量最大的样本进行下一步标注。我们证明，与传统的监督学习方法相比，我们的方法在达到相似F1分数时所需的标注样本显著更少，仅需使用训练集的59%。我们还研究了类别平衡的分布，以建立标注指南。