论文标题
深层监督信息的瓶颈散列散布于跨模式检索的计算机辅助诊断
Deep Supervised Information Bottleneck Hashing for Cross-modal Retrieval based Computer-aided Diagnosis
论文作者
论文摘要
将X射线图像，放射学报告和其他医学数据映射为公共空间中的二元代码，可以帮助临床医生从异质模式（即基于Hashing基于Hashing的跨模式医学数据检索）中检索与病理相关的数据，从而提供了一种新的视图，以促进计算机所诊断。然而，仍然存在提高医疗检索准确性的障碍：如何在不分散多余信息注意力的情况下揭示医学数据的模棱两可的语义。为了避免这一缺点，我们提出了深层监督的信息瓶颈哈希（DSIBH），从而有效地增强了哈希码的可区分性。具体而言，单个模态的深层确定性信息瓶颈（Yu，Yu和Principe 2021）扩展到跨模式场景。从中受益，多余的信息减少了，这有助于哈希码的可区分性。实验结果表明，与跨模式医疗数据检索任务中最新的DSIBH相比，所提出的DSIBH的精度具有较高的准确性。
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.
