Learning Binary Semantic Embedding for Large-Scale Breast Histology Image Analysis

With the progress of clinical imaging innovation and machine learning, the computer-assisted diagnosis of breast histology images has attracted broad attention. Nonetheless, the use of computer-assisted diagnoses has been blocked due to the incomprehensibility of customary classification models. In view of this question, we propose a novel method for L earning B inary S emantic E mbedding (LBSE). In this study, bit balance and uncorrelation constraints, double supervision, discrete optimization and asymmetric pairwise similarity are seamlessly integrated for learning binary semantic-preserving embedding. Moreover, a fusion-based strategy is carefully designed to handle the intractable problem of parameter setting, saving huge amounts of time for boundary tuning. Based on the above-mentioned proficient and effective embedding, classification and retrieval are simultaneously performed to give interpretable image-based deduction and model helped conclusions for breast histology images. Extensive experiments are conducted on three benchmark datasets to approve the predominance of LBSE in different situations.

Authors

• Xingbo Liu School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, China
• Xiao Kang School of Software, Shandong University, Jinan 250101, China
• Xiushan Nie School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, China Shandong Yunhai Guochuang Cloud Computing Equipment Industry Innovation Co., Ltd, Jinan, China
• Jie Guo Xidian University
• Shaohua Wang Central University of Finance and Economics
• Yilong Yin School of Software, Shandong University, Jinan 250101, China

Keywords

• Semantics
• Histopathology
• Optimization
• Computer aided diagnosis
• Codes
• Image retrieval
• Cognition
• Semantic Embedding
• Histological Image Analysis
• Medical Imaging
• Computationally Intractable
• Pairwise Similarity
• Computer-aided Diagnosis
• Balance Constraints
• Hyperparameters
• Classification Performance
• Classification Task
• Semantic Information
• Hash Function
• Orthogonal Matrix
• Binary Code
• Discrete Method
• Pairwise Relationships
• Nearest Neighbor Search
• Fusion Strategy
• Closest Neighbors
• Content-based Image Retrieval
• Binary Space
• Semantic Labels
• Binary Bits
• Retrieval Performance
• Code Length
• Relaxation Method
• Linear Projection
• Auxiliary Variables
• Breast Cancer Patients
• Approximate nearest neighbor search
• binary embedding
• breast cancer
• computer-assisted diagnosis
• histology image