Organ-Regional AI Framework Improves Radiology Report Generation

Organ-Related Information Processing in Radiology: A New Approach to Automated Report Generation

The automated creation of radiology reports (Radiology Report Generation, RRG) using Artificial Intelligence (AI) promises significant relief for radiologists. A promising approach in this area is the Organ-Regional Information Driven (ORID) Framework, which is the focus of this article.

Challenges and Previous Approaches in RRG

Generating accurate and comprehensive radiology reports based on image data presents a complex challenge. Previous AI-powered RRG methods have primarily focused on adaptations of the encoder-decoder model architecture. These models analyze the image data and translate it into text. One problem with this is the integration of multimodal information, i.e., the combination of image data with other medical data, as well as filtering out irrelevant information, for example, from organs that are not the focus of the examination.

The ORID Framework: An Organ-Related Approach

The ORID Framework addresses these challenges and pursues a new approach that specifically considers the information from different organ regions. The framework is based on the LLaVA-Med model and integrates several innovative modules:

First, an RRG-specific instruction dataset is created to improve the model's ability to describe organ-regional diagnoses. The resulting model is referred to as LLaVA-Med-RRG.

Another core component of the ORID Framework is an organ-based, cross-modal fusion module. This module effectively combines the information from the organ-regional diagnosis description with the radiological image data. This enables a more precise analysis.

To minimize the influence of irrelevant information originating from unaffected organs, an organ importance analysis module is used. This module utilizes Graph Neural Networks (GNN) to examine the connections of the cross-modal information from each organ region. By weighting the information based on its relevance, the accuracy of the report generation is further increased.

Evaluation and Outlook

Extensive experiments and comparisons with established methods demonstrate the effectiveness of the ORID Framework. The results show that the organ-related approach leads to improved accuracy and coherence of the generated reports. The ORID Framework has the potential to significantly advance the automation of radiology report generation and optimize workflows in radiology. Further research is necessary to fully exploit the framework's potential in clinical practice. Of particular interest is the application of the framework to other imaging modalities and the integration of further medical data to make report generation even more comprehensive and precise. The development of AI-powered systems like Mindverse, which integrate such frameworks, will significantly influence the future of medical diagnostics.

Bibliographie Gu, T., Yang, K., An, X., Feng, Z., Liu, D., & Cai, W. (2024). ORID: Organ-Regional Information Driven Framework for Radiology Report Generation. arXiv preprint arXiv:2411.13025. Sze