Dual-channel Prototype Network for few-shot Classification of Pathological Images

TL;DR

This paper introduces the Dual-channel Prototype Network (DCPN), a novel method combining transformer and CNN features for effective few-shot classification of pathological images, addressing data scarcity in medical diagnostics.

Contribution

The paper presents a dual-channel architecture that enhances prototype representations for few-shot pathological image classification, integrating self-supervised learning with PVT and CNNs.

Findings

DCPN outperforms existing methods in few-shot classification accuracy.

DCPN achieves supervised learning benchmarks in same-domain tasks.

The approach demonstrates robustness across multiple pathological datasets.

Abstract

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.