Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation (1903.10122v1)

Abstract: Generating long and semantic-coherent reports to describe medical images poses great challenges towards bridging visual and linguistic modalities, incorporating medical domain knowledge, and generating realistic and accurate descriptions. We propose a novel Knowledge-driven Encode, Retrieve, Paraphrase (KERP) approach which reconciles traditional knowledge- and retrieval-based methods with modern learning-based methods for accurate and robust medical report generation. Specifically, KERP decomposes medical report generation into explicit medical abnormality graph learning and subsequent natural LLMing. KERP first employs an Encode module that transforms visual features into a structured abnormality graph by incorporating prior medical knowledge; then a Retrieve module that retrieves text templates based on the detected abnormalities; and lastly, a Paraphrase module that rewrites the templates according to specific cases. The core of KERP is a proposed generic implementation unit---Graph Transformer (GTR) that dynamically transforms high-level semantics between graph-structured data of multiple domains such as knowledge graphs, images and sequences. Experiments show that the proposed approach generates structured and robust reports supported with accurate abnormality description and explainable attentive regions, achieving the state-of-the-art results on two medical report benchmarks, with the best medical abnormality and disease classification accuracy and improved human evaluation performance.

Overview of "Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation"

The paper "Knowledge-driven Encode, Retrieve, Paraphrase (KERP) for Medical Image Report Generation" introduces a refined approach to generating semantic-coherent and informative reports based on medical images. This research addresses the complex challenge of bridging visual and linguistic modalities while incorporating structured medical knowledge to enhance report accuracy and comprehensibility.

Core Contributions

The KERP model presents a novel methodology for medical report generation by integrating multiple processing stages: encoding visual input as structured data, retrieving relevant text templates, and paraphrasing these templates to fit specific contexts. The process is underpinned by a newly proposed Graph Transformer (GTR) unit capable of manipulating and translating complex graph structures across different domains.

1. Knowledge-driven Encoding: The process begins with transforming visual features into an abnormality graph. This graph encapsulates prior medical knowledge, aiding in the identification of clinically significant features within the image.
2. Template Retrieval: The detected abnormalities guide the retrieval of text templates that broadly match the identified visual phenomena. This retrieval process is attentive to the structured representation of the visual information.
3. Paraphrase Generation: Finally, the templates are rewritten to reflect nuanced case-specific details through a paraphrasing mechanism. This ensures that the generated reports are not only accurate but also contextually tailored.

Experimental Results

The KERP model was evaluated on two benchmarks: IU X-Ray and CX-CHR datasets. It achieved superior results in generating well-structured reports with enhanced abnormality and disease classification accuracy. The following are notable metrics:

• On the IU X-Ray dataset, the method showed notable improvements in BLEU scores, surpassing baseline models, indicating its capability to generate nearer textual content to the reference text.
• In abnormality and disease classification, KERP demonstrated high AUC scores, indicating efficient incorporation of domain-specific medical knowledge, which significantly aids in precise clinical reporting.

Graph Transformer (GTR)

A distinctive aspect of this research is the GTR, a dynamic mechanism facilitating high-level transformations between graph data structures. This flexible unit supports multiple graph domains—such as images and sequences—via a robust attention mechanism. The GTR allows for efficient data handling, ensuring consistency in transforming complex visual data into linguistic outputs.

Implications and Future Directions

The implications of KERP are substantial within the AI and healthcare sectors. By effectively bridging the gap between complex visual data and linguistic output, the model can significantly enhance automated report generation's accuracy and reliability. The research showcases how hybrid architectures, which blend traditional knowledge-based approaches with modern learning techniques, can achieve superior performance in handling domain-specific tasks.

Future research could focus on expanding the application of GTR to other multi-modal tasks beyond medical reporting, considering its potential for flexible information translation across diverse domains. Additionally, improvements may be explored in incorporating even more nuanced medical knowledge and patient data to further personalize reports.

This paper represents a step forward in the pursuit of more intelligent, reliable AI systems capable of sophisticated decision-making and language processing, especially within the field of medical diagnosis and reporting.