In pathology, the rarity of certain diseases and the complexity in annotating pathological images significantly hinder the creation of extensive, high-quality datasets. This limitation impedes the progress of deep learning-assisted diagnostic systems in pathology. Consequently, it becomes imperative to devise a technology that can discern new disease categories from a minimal number of annotated examples. Such a technology would substantially advance deep learning models for rare diseases. Addressing this need, we introduce the Dual-channel Prototype Network (DCPN), rooted in the few-shot learning paradigm, to tackle the challenge of classifying pathological images with limited samples. DCPN augments the Pyramid Vision Transformer (PVT) framework for few-shot classification via self-supervised learning and integrates it with convolutional neural networks. This combination forms a dual-channel architecture that extracts multi-scale, highly precise pathological features. The approach enhances the versatility of prototype representations and elevates the efficacy of prototype networks in few-shot pathological image classification tasks. We evaluated DCPN using three publicly available pathological datasets, configuring small-sample classification tasks that mirror varying degrees of clinical scenario domain shifts. Our experimental findings robustly affirm DCPN's superiority in few-shot pathological image classification, particularly in tasks within the same domain, where it achieves the benchmarks of supervised learning.

翻译：在病理学中，某些疾病的罕见性以及病理图像标注的复杂性严重阻碍了大规模高质量数据集的构建。这一限制制约了深度学习辅助诊断系统在病理学中的进展。因此，开发一种能够从少量标注样本中识别新疾病类别的技术变得至关重要。这类技术将极大推动罕见病深度学习模型的发展。为应对这一需求，我们提出了基于小样本学习范式的双通道原型网络（DCPN），以解决有限样本下的病理图像分类难题。DCPN通过自监督学习增强了金字塔视觉Transformer（PVT）框架在小样本分类中的能力，并将其与卷积神经网络集成。这一组合形成了双通道架构，能够提取多尺度、高精度的病理特征。该方法增强了原型表示的通用性，提升了原型网络在小样本病理图像分类任务中的效能。我们利用三个公开病理数据集对DCPN进行评估，配置了模拟临床场景域偏移不同程度的小样本分类任务。实验结果有力地证实了DCPN在少样本病理图像分类中的优越性，特别是在同域任务中，其性能达到了监督学习的基准水平。