HYDEN: Hyperbolic Density Representations for Medical Images and Reports
This work addresses semantic uncertainty in medical image-text analysis, which is an incremental improvement for the medical AI domain.
The paper tackled the problem of semantic uncertainty in medical image-text representation learning, where images and text can have multiple interpretations, by proposing HYDEN, a hyperbolic density embedding approach that achieved superior performance in zero-shot tasks compared to baseline methods.
In light of the inherent entailment relations between images and text, hyperbolic point vector embeddings, leveraging the hierarchical modeling advantages of hyperbolic space, have been utilized for visual semantic representation learning. However, point vector embedding approaches fail to address the issue of semantic uncertainty, where an image may have multiple interpretations, and text may refer to different images, a phenomenon particularly prevalent in the medical domain. Therefor, we propose \textbf{HYDEN}, a novel hyperbolic density embedding based image-text representation learning approach tailored for specific medical domain data. This method integrates text-aware local features alongside global features from images, mapping image-text features to density features in hyperbolic space via using hyperbolic pseudo-Gaussian distributions. An encapsulation loss function is employed to model the partial order relations between image-text density distributions. Experimental results demonstrate the interpretability of our approach and its superior performance compared to the baseline methods across various zero-shot tasks and different datasets.