Dual-channel Prototype Network for few-shot Classification of
  Pathological Images
Hao Quan Xinjia Li Dayu Hu Tianhang Nan Xiaoyu Cui
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.