Learning Robust Hash Codes for Multiple Instance Image Retrieval
This work addresses the challenge of large-scale image retrieval in medical imaging, where tumors coexist with benign masses, by introducing a multiple instance deep hashing technique.
The paper tackles the problem of learning robust hash codes for image retrieval under weak bag-level supervision, specifically for tumor assessment, and demonstrates improved retrieval performance on benchmark mammography and histology datasets.
In this paper, for the first time, we introduce a multiple instance (MI) deep hashing technique for learning discriminative hash codes with weak bag-level supervision suited for large-scale retrieval. We learn such hash codes by aggregating deeply learnt hierarchical representations across bag members through a dedicated MI pool layer. For better trainability and retrieval quality, we propose a two-pronged approach that includes robust optimization and training with an auxiliary single instance hashing arm which is down-regulated gradually. We pose retrieval for tumor assessment as an MI problem because tumors often coexist with benign masses and could exhibit complementary signatures when scanned from different anatomical views. Experimental validations on benchmark mammography and histology datasets demonstrate improved retrieval performance over the state-of-the-art methods.