  Mapping X-ray images, radiology reports, and other medical data as binary
codes in the common space, which can assist clinicians to retrieve
pathology-related data from heterogeneous modalities (i.e., hashing-based
cross-modal medical data retrieval), provides a new view to promot
computeraided diagnosis. Nevertheless, there remains a barrier to boost medical
retrieval accuracy: how to reveal the ambiguous semantics of medical data
without the distraction of superfluous information. To circumvent this
drawback, we propose Deep Supervised Information Bottleneck Hashing (DSIBH),
which effectively strengthens the discriminability of hash codes. Specifically,
the Deep Deterministic Information Bottleneck (Yu, Yu, and Principe 2021) for
single modality is extended to the cross-modal scenario. Benefiting from this,
the superfluous information is reduced, which facilitates the discriminability
of hash codes. Experimental results demonstrate the superior accuracy of the
proposed DSIBH compared with state-of-the-arts in cross-modal medical data
retrieval tasks.
