Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation (1805.08298v2)

Abstract: Generating long and coherent reports to describe medical images poses challenges to bridging visual patterns with informative human linguistic descriptions. We propose a novel Hybrid Retrieval-Generation Reinforced Agent (HRGR-Agent) which reconciles traditional retrieval-based approaches populated with human prior knowledge, with modern learning-based approaches to achieve structured, robust, and diverse report generation. HRGR-Agent employs a hierarchical decision-making procedure. For each sentence, a high-level retrieval policy module chooses to either retrieve a template sentence from an off-the-shelf template database, or invoke a low-level generation module to generate a new sentence. HRGR-Agent is updated via reinforcement learning, guided by sentence-level and word-level rewards. Experiments show that our approach achieves the state-of-the-art results on two medical report datasets, generating well-balanced structured sentences with robust coverage of heterogeneous medical report contents. In addition, our model achieves the highest detection accuracy of medical terminologies, and improved human evaluation performance.

Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation

The paper entitled "Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation" introduces the HRGR-Agent, a sophisticated model designed to address the intricate task of generating comprehensive medical reports from image data. This work merges traditional retrieval-based methodologies with innovative learning-based techniques to generate structured, coherent, and diverse medical reports, overcoming some of the limitations seen in current captioning models that are typically insufficient for such complex tasks.

Model Framework

The HRGR-Agent incorporates a hierarchical decision-making framework that alternates between template retrieval and text generation to produce medical reports. This hybrid approach involves two key modules:

• Retrieval Policy Module: This module makes high-level decisions to either retrieve pre-formed template sentences from a database or to delegate the task to a generative module for crafting novel sentences.
• Generation Module: Upon activation, this module utilizes an RNN architecture augmented with attention mechanisms to generate sentences word-by-word from scratch, thus offering the necessary flexibility to describe rare or abnormal findings.

Reinforcement learning plays a crucial role in training the HRGR-Agent. The model is guided by both sentence-level and word-level rewards, optimizing its performance not merely on textual coherence and fluency but also on the correctness and informativeness of the generated reports.

Empirical Evaluation

The HRGR-Agent's performance is extensively validated on two medical report datasets: the Indiana University Chest X-Ray Collection and a proprietary dataset (CX-CHR). The model demonstrates significant superiority on multiple fronts:

• Automatic Metrics: HRGR-Agent sets new benchmarks in metrics such as CIDEr and BLEU scores, underscoring its adeptness at generating accurate and meaningful text that parallels expert analysis.
• Medical Abnormality Detection: The model attains the highest accuracy in detecting medical abnormalities, an essential capability to ensure reports' diagnostic value.
• Human Evaluation: Surveys conducted to assess human preferences further reinforce the model’s strengths, showing a marked preference for reports generated by the HRGR-Agent compared to other models.

Theoretical Contributions

The paper’s contributions extend beyond empirical enhancement. The integration of retrieval and generation in a single architecture optimizes content selection strategies that balance simplicity and complexity in report generation. Reinforcement learning is not only applied to sequence generation but is innovatively extended to manage the interplay between retrieval and generation, thereby enhancing both immediate and accumulative report generation quality.

Implications and Future Directions

The HRGR-Agent sets a precedent for automatically generating detailed medical reports that could substantially alleviate the workload on radiologists and enhance consistency and accuracy in diagnosing conditions from medical images. The method’s nuanced balance between template retrieval and text generation propagates potential advancements across other domains where structured document generation from data is requisite.

Future research could explore adaptive learning mechanisms within the HRGR-Agent to personalize medical report styles according to specific institutional or practitioner preferences. Furthermore, extending these techniques to multi-modal settings with additional contextual data could broaden the system's applicability, enabling comprehensive diagnostic assistance across diverse healthcare scenarios.

In conclusion, the HRGR-Agent represents a significant stride in medical AI, providing a robust framework for integrating generations with retrieval commands to deliver detailed, accurate medical narratives from image-based data.