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Medical Report Generation Is A Multi-label Classification Problem

Yijian Fan, Zhenbang Yang, Rui Liu, Mingjie Li, Xiaojun Chang

TL;DR

The paper reframes medical report generation (MRG) as a multi-label classification problem over a refined radiology knowledge graph, integrated with a BLIP-based generator. By classifying clinically relevant knowledge-graph nodes using a knowledge encoder and fusing them with image features via cross-attention, the approach shifts away from complex decoders toward targeted keyword prediction, trained with image-text contrastive, image-text matching, and language modeling losses. Extending this to IU-Xray and MIMIC-CXR, the method achieves state-of-the-art metrics (BLEU, METEOR, CIDEr) and demonstrates that higher node classification accuracy translates into higher-quality reports, while highlighting challenges posed by long-tailed label distributions. The work suggests that knowledge-graph refinement and classification-centric MRG can improve efficiency and accuracy in clinical reporting, with potential for broader adoption and future improvements in handling rare but critical conditions.

Abstract

Medical report generation is a critical task in healthcare that involves the automatic creation of detailed and accurate descriptions from medical images. Traditionally, this task has been approached as a sequence generation problem, relying on vision-and-language techniques to generate coherent and contextually relevant reports. However, in this paper, we propose a novel perspective: rethinking medical report generation as a multi-label classification problem. By framing the task this way, we leverage the radiology nodes from the commonly used knowledge graph, which can be better captured through classification techniques. To verify our argument, we introduce a novel report generation framework based on BLIP integrated with classified key nodes, which allows for effective report generation with accurate classification of multiple key aspects within the medical images. This approach not only simplifies the report generation process but also significantly enhances performance metrics. Our extensive experiments demonstrate that leveraging key nodes can achieve state-of-the-art (SOTA) performance, surpassing existing approaches across two benchmark datasets. The results underscore the potential of re-envisioning traditional tasks with innovative methodologies, paving the way for more efficient and accurate medical report generation.

Medical Report Generation Is A Multi-label Classification Problem

TL;DR

The paper reframes medical report generation (MRG) as a multi-label classification problem over a refined radiology knowledge graph, integrated with a BLIP-based generator. By classifying clinically relevant knowledge-graph nodes using a knowledge encoder and fusing them with image features via cross-attention, the approach shifts away from complex decoders toward targeted keyword prediction, trained with image-text contrastive, image-text matching, and language modeling losses. Extending this to IU-Xray and MIMIC-CXR, the method achieves state-of-the-art metrics (BLEU, METEOR, CIDEr) and demonstrates that higher node classification accuracy translates into higher-quality reports, while highlighting challenges posed by long-tailed label distributions. The work suggests that knowledge-graph refinement and classification-centric MRG can improve efficiency and accuracy in clinical reporting, with potential for broader adoption and future improvements in handling rare but critical conditions.

Abstract

Medical report generation is a critical task in healthcare that involves the automatic creation of detailed and accurate descriptions from medical images. Traditionally, this task has been approached as a sequence generation problem, relying on vision-and-language techniques to generate coherent and contextually relevant reports. However, in this paper, we propose a novel perspective: rethinking medical report generation as a multi-label classification problem. By framing the task this way, we leverage the radiology nodes from the commonly used knowledge graph, which can be better captured through classification techniques. To verify our argument, we introduce a novel report generation framework based on BLIP integrated with classified key nodes, which allows for effective report generation with accurate classification of multiple key aspects within the medical images. This approach not only simplifies the report generation process but also significantly enhances performance metrics. Our extensive experiments demonstrate that leveraging key nodes can achieve state-of-the-art (SOTA) performance, surpassing existing approaches across two benchmark datasets. The results underscore the potential of re-envisioning traditional tasks with innovative methodologies, paving the way for more efficient and accurate medical report generation.
Paper Structure (14 sections, 5 equations, 7 figures, 2 tables)

This paper contains 14 sections, 5 equations, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Clinical Potential
  3. Progress in Medical Report Generation
  4. Rethinking and New Perspective
  5. Contribution
  6. Related Work
  7. Method
  8. Model Architecture
  9. Training Objective
  10. Experiment
  11. Setting
  12. Main result
  13. Discussion
  14. Conclusion

Figures (7)

  • Figure 1: A sample from the IU X-ray dataset and the widely used knowledge graph. The red words are the nodes mentioned in the ground truth report.
  • Figure 2: The overview of our proposed framework, which consists of a knowledge encoder, image encoder, text encoder, multi-modal encoder, and a decoder. The knowledge encoder integrates classified nodes with visual representations.
  • Figure 3: Label distribution in the IU-Xray dataset.
  • Figure 4: Label distribution in the MIMIC-CXR dataset.
  • Figure 5: Changes on natural general metrics along with node accuracy on the IU-Xray dataset.
  • ...and 2 more figures