Deep Supervised Information Bottleneck Hashing for Cross-modal Retrieval based Computer-aided Diagnosis
This work addresses a domain-specific problem for clinicians by enhancing computer-aided diagnosis through more accurate retrieval of pathology-related data from heterogeneous medical modalities, but it is incremental as it extends an existing method to a cross-modal scenario.
The paper tackled the problem of improving cross-modal medical data retrieval accuracy by reducing superfluous information, proposing Deep Supervised Information Bottleneck Hashing (DSIBH), which achieved superior accuracy compared to state-of-the-art methods in experiments.
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.