| Paper ID | BIO-8.1 |
| Paper Title |
LEARNING BINARY SEMANTIC EMBEDDING FOR BREAST HISTOLOGY IMAGE CLASSIFICATION AND RETRIEVAL |
| Authors |
Xiao Kang, Xingbo Liu, Shandong University, China; Xiushan Nie, Shandong Jianzhu University, China; Yilong Yin, Shandong University, China |
| Session | BIO-8: Biological Image Analysis |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Biomedical Imaging and Signal Processing: [BIO-BIA] Biological image analysis |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
With the development of medical imaging technology and machine learning, the computer-assisted diagnosis has attracted extensive research attention, which can provide beneficial reference to pathologists. However, the exponential growth of medical images and uninterpretability of traditional classification models have hindered the applications of the computer-assisted diagnosis. To address this issues, we propose a novel method for Learning Binary Semantic Embedding (LBSE). Based on this 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. |
