  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.
