Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

HYDEN: Hyperbolic Density Representations for Medical Images and Reports

Zhi Qiao, Linbin Han, Xiantong Zhen, Jia-Hong Gao, Zhen Qian

TL;DR

HYDEN introduces hyperbolic density embeddings for medical image–text representation to address semantic uncertainty and hierarchical structure inherent in clinical data. By combining text-aware local image features with global representations, and mapping them to density distributions in hyperbolic space via a pseudo-hyperbolic Gaussian, HYDEN leverages encapsulation losses and contrastive objectives to align image–text distributions. The approach demonstrates superior zero-shot performance on medical classification and retrieval tasks and offers interpretable visual-semantic hierarchies, validated on the MIMIC-CXR v2 dataset. This work advances beyond Euclidean point embeddings and prior hyperbolic methods by modeling distributions rather than points, enabling more robust handling of uncertainty and partial orders in medical narratives. It provides a principled framework for medical cross-modal understanding with potential impact on zero-shot diagnosis, report-text alignment, and retrieval systems.

Abstract

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.

HYDEN: Hyperbolic Density Representations for Medical Images and Reports

TL;DR

HYDEN introduces hyperbolic density embeddings for medical image–text representation to address semantic uncertainty and hierarchical structure inherent in clinical data. By combining text-aware local image features with global representations, and mapping them to density distributions in hyperbolic space via a pseudo-hyperbolic Gaussian, HYDEN leverages encapsulation losses and contrastive objectives to align image–text distributions. The approach demonstrates superior zero-shot performance on medical classification and retrieval tasks and offers interpretable visual-semantic hierarchies, validated on the MIMIC-CXR v2 dataset. This work advances beyond Euclidean point embeddings and prior hyperbolic methods by modeling distributions rather than points, enabling more robust handling of uncertainty and partial orders in medical narratives. It provides a principled framework for medical cross-modal understanding with potential impact on zero-shot diagnosis, report-text alignment, and retrieval systems.

Abstract

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.
Paper Structure (14 sections, 10 equations, 3 figures, 5 tables)

This paper contains 14 sections, 10 equations, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Preliminaries
  4. Method
  5. Image-Text Feature Embedding
  6. Hyperbolic Density Embedding
  7. Loss Function Based on Density Embedding
  8. Experiments
  9. Training Details
  10. Quantitative Analysis
  11. Qualitative Analysis
  12. Conclusion
  13. Material
  14. Evaluation Tasks & Data

Figures (3)

  • Figure 1: (a) Depiction of the visual-semantic hierarchy in the medical text-image domain, illustrating how different medical concepts are organized and interconnected with each other and with medical images. (b) Representation of medical data embeddings transitioning from Euclidean to hyperbolic space to effectively capture and represent the density partial ordering, while maintaining the integrity of relative density relationships.
  • Figure 2: Framework of HYDEN: The contrastive loss function utilizes the negative Lorentzian distance as a metric for similarity. Additionally, an encapsulation loss is employed to enforce the density partial ordering of image and text embeddings within the representation space.
  • Figure 3: Distribution of embedding distances from [ROOT]: We embed 3858 testing images and text from the MIMIC-CXR v2 dataset using pre-trained CLIP, MERU, and HYDEN models.