Zhong Lanfeng, Qian Kun, Liao Xin, Huang Zongyao, Liu Yang, Zhang Shaoting, Wang Guotai
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Department of Information and Intelligence Development, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200032, China.
Med Image Anal. 2025 May;102:103542. doi: 10.1016/j.media.2025.103542. Epub 2025 Mar 12.
Histopathological image classification using deep learning is crucial for accurate and efficient cancer diagnosis. However, annotating a large amount of histopathological images for training is costly and time-consuming, leading to a scarcity of available labeled data for training deep neural networks. To reduce human efforts and improve efficiency for annotation, we propose a Unified Semi-supervised Active Learning framework (UniSAL) that effectively selects informative and representative samples for annotation. First, unlike most existing active learning methods that only train from labeled samples in each round, dual-view high-confidence pseudo training is proposed to utilize both labeled and unlabeled images to train a model for selecting query samples, where two networks operating on different augmented versions of an input image provide diverse pseudo labels for each other, and pseudo label-guided class-wise contrastive learning is introduced to obtain better feature representations for effective sample selection. Second, based on the trained model at each round, we design novel uncertain and representative sample selection strategy. It contains a Disagreement-aware Uncertainty Selector (DUS) to select informative uncertain samples with inconsistent predictions between the two networks, and a Compact Selector (CS) to remove redundancy of selected samples. We extensively evaluate our method on three public pathological image classification datasets, i.e., CRC5000, Chaoyang and CRC100K datasets, and the results demonstrate that our UniSAL significantly surpasses several state-of-the-art active learning methods, and reduces the annotation cost to around 10% to achieve a performance comparable to full annotation. Code is available at https://github.com/HiLab-git/UniSAL.
使用深度学习进行组织病理学图像分类对于准确高效的癌症诊断至关重要。然而，标注大量用于训练的组织病理学图像成本高昂且耗时，导致用于训练深度神经网络的可用标注数据稀缺。为了减少人工工作量并提高标注效率，我们提出了一种统一的半监督主动学习框架（UniSAL），该框架能有效选择信息丰富且具有代表性的样本进行标注。首先，与大多数现有的主动学习方法不同，后者在每一轮仅从标注样本进行训练，我们提出了双视图高置信度伪训练，利用标注图像和未标注图像来训练一个用于选择查询样本的模型，其中在输入图像的不同增强版本上运行的两个网络为彼此提供不同的伪标签，并引入伪标签引导的类内对比学习以获得更好的特征表示，从而实现有效的样本选择。其次，基于每一轮训练得到的模型，我们设计了新颖的不确定且具有代表性的样本选择策略。它包含一个分歧感知不确定性选择器（DUS），用于选择两个网络预测不一致的信息丰富的不确定样本，以及一个紧凑选择器（CS），用于去除所选样本的冗余。我们在三个公共病理图像分类数据集，即CRC5000、朝阳和CRC100K数据集上广泛评估了我们的方法，结果表明我们的UniSAL显著超越了几种先进的主动学习方法，并将标注成本降低到约10%，以实现与完全标注相当的性能。代码可在https://github.com/HiLab-git/UniSAL获取。
