  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.
