Learning Binary Semantic Embedding for Histology Image Classification and Retrieval

TL;DR

This paper introduces LBSE, a novel binary embedding method for histology image classification and retrieval, enhancing interpretability and efficiency in computer-assisted diagnosis.

Contribution

The paper presents a new binary semantic embedding approach with double supervision, uncorrelation, balance constraints, and discrete optimization for histology image analysis.

Findings

LBSE outperforms existing methods on benchmark datasets.

The method improves interpretability and retrieval accuracy.

Experiments validate robustness across various scenarios.

Abstract

With the development of medical imaging technology and machine learning, computer-assisted diagnosis which can provide impressive reference to pathologists, attracts extensive research interests. The exponential growth of medical images and uninterpretability of traditional classification models have hindered the applications of computer-assisted diagnosis. To address these issues, we propose a novel method for Learning Binary Semantic Embedding (LBSE). Based on the efficient and effective embedding, classification and retrieval are performed to provide interpretable computer-assisted diagnosis for histology images. Furthermore, double supervision, bit uncorrelation and balance constraint, asymmetric strategy and discrete optimization are seamlessly integrated in the proposed method for learning binary embedding. Experiments conducted on three benchmark datasets validate the superiority of LBSE under various scenarios.