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

• URL: http://arxiv.org/abs/2311.07871v1
• Date: Tue, 14 Nov 2023 03:03:21 GMT
• Title: Dual-channel Prototype Network for few-shot Classification of Pathological Images
• Authors: Hao Quan, Xinjia Li, Dayu Hu, Tianhang Nan and Xiaoyu Cui
• Abstract summary: We introduce the Dual-channel Prototype Network (DCPN) to tackle the challenge of classifying pathological images with limited samples. DCPN augments the Pyramid Vision Transformer 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.
• Score: 0.7562219957261347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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