Open AccessSemi‐supervised breast histopathological image classification with self‐training based on non‐linear distance metric
Author(s) -
Liu Kun,
Liu Zhuolin,
Liu Sidong
Publication year - 2022
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12548
Subject(s) - artificial intelligence , computer science , pattern recognition (psychology) , metric (unit) , medical diagnosis , consistency (knowledge bases) , deep learning , machine learning , medicine , radiology , operations management , economics
Abstract Histopathological analysis requires a lot of clinical experience and time for pathologists. Artificial intelligence (AI) may have an important role in assisting pathologists and leading to more efficient and effective histopathological diagnoses. To address the challenge of requiring a large number of labelled images to train deep learning models in breast cancer histopathological image classification, a self‐training semi‐supervised learning method consisting three components is proposed: Firstly, a pre‐trained ResNet‐18 was used to extract features and generate pseudo‐labels for unlabelled data; secondly, a relational weight network based on the squeeze‐and‐excitation network (SENet) was trained to calculate the non‐linear distance metrices between labelled and unlabelled samples, in order to improve the accuracy of pseudo‐labelling; lastly, a consistency loss—maximum mean difference (MMD)—was added into the model to minimize the divergence between distributions of unlabelled and labelled samples. Extensive experiments were conducted on the open access BreakHis dataset. The proposed method outperformed the state‐of‐the‐art semi‐supervised methods at all tested annotated percentages (10–70%), and also achieved comparable performance with supervised methods at higher annotated percentages (50%, 70%).