Semi-Supervised Breast Histological Image Classification by Node-Attention Graph Transfer Network

Liheng Gong,Xiao Zhang,Jingjing Yang

doi:10.1109/access.2020.3020149

Abstract

As a major cause of leading female death, breast cancer is often diagnosed by histological images which has been resolved by many deep learning methods with the assistance of large amounts of annotated data. However, their performances are severely limited by the lack of sufficient labeled data in clinical practice. This paper aims to relieve the annotating workload by a semi-supervised transfer learning algorithm to conduct knowledge distillation from a completely labeled source domain. To achieve this goal, we propose a node-attention graph transfer network to exploit the inherent correlation between individual samples by graph convolutional network, along with a cross-domain graph learning module to stimulate the graph construction in target domain. In the meanwhile, we design a node-attention mechanism to learn the individual contribution of each source image for target domain, which can further leverage the domain-gap by our node-attention transfer learning. Results of semi-supervised breast histological image classification with various scales of annotated training images are performable and further experiments demonstrate the significant contributions of each component we proposed.

Highlights

Breast cancer has severe health threats to women due to its considerable patients, who suffer from a rigorous mortality in the whole world
Aiming to learn graph representation in the second feature learning stage, we introduce graph convolutional network to exploit the inherent correlations between the samples both in source and target domains, where Graph Convolutional Network (GCN) has been proved its high-efficiency in several semi-supervised learning methods [12], [15]
Semi-supervised learning of breast histological image classification provides an effective solution to the lacking of sufficient annotated data in practice

Summary

Introduction

Breast cancer has severe health threats to women due to its considerable patients, who suffer from a rigorous mortality in the whole world. The histology examination can obtain sufficient images within a limited period to provide reliable diagnostic evidence, where costs large amounts of manpower to conclude the clinical manifestation even with misdiagnosis accidentally To make this laborious work easier to execute, a number of automatic breast cancer diagnosis. The first category of hand-crafted feature based methods employs prior knowledge to design robust feature representations, and utilize independent classifier on them [5], [25], [28] Another solution for breast histological image classification is based on deep learning technology, with the developments of computing power (GPUs) in recent years, which combines feature learning and classification into an unified framework [17], [20], [40]. In contrast to general image annotation, the symbol in breast histological image is more complicated to discover

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Conclusion