Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation (2106.06963v2)

Abstract: Automatically generating radiology reports can improve current clinical practice in diagnostic radiology. On one hand, it can relieve radiologists from the heavy burden of report writing; On the other hand, it can remind radiologists of abnormalities and avoid the misdiagnosis and missed diagnosis. Yet, this task remains a challenging job for data-driven neural networks, due to the serious visual and textual data biases. To this end, we propose a Posterior-and-Prior Knowledge Exploring-and-Distilling approach (PPKED) to imitate the working patterns of radiologists, who will first examine the abnormal regions and assign the disease topic tags to the abnormal regions, and then rely on the years of prior medical knowledge and prior working experience accumulations to write reports. Thus, the PPKED includes three modules: Posterior Knowledge Explorer (PoKE), Prior Knowledge Explorer (PrKE) and Multi-domain Knowledge Distiller (MKD). In detail, PoKE explores the posterior knowledge, which provides explicit abnormal visual regions to alleviate visual data bias; PrKE explores the prior knowledge from the prior medical knowledge graph (medical knowledge) and prior radiology reports (working experience) to alleviate textual data bias. The explored knowledge is distilled by the MKD to generate the final reports. Evaluated on MIMIC-CXR and IU-Xray datasets, our method is able to outperform previous state-of-the-art models on these two datasets.

• The paper introduces the PPKED framework to mitigate visual and textual biases in radiology report generation.
• It leverages three modules—PoKE, PrKE, and MKD—with an Adaptive Distilling Attention mechanism to fuse heterogeneous knowledge sources.
• Experimental results on MIMIC-CXR and IU-Xray datasets show improved BLEU, METEOR, ROUGE-L, and CIDEr scores, enhancing diagnostic accuracy.

Overview of the Paper on Radiology Report Generation

The paper "Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation" presents a sophisticated framework known as Posterior-and-Prior Knowledge Exploring-and-Distilling (PPKED) to automate the generation of radiology reports. This task is critical within the field of diagnostic radiology as it has the potential to significantly reduce the workload of radiologists and mitigate risks associated with misdiagnosis. The paper addresses the challenges associated with this task, particularly visual and textual data biases, which have historically impeded the application of data-driven neural networks in this domain.

Core Components of the PPKED Framework

The PPKED framework is composed of three integral modules: Posterior Knowledge Explorer (PoKE), Prior Knowledge Explorer (PrKE), and Multi-domain Knowledge Distiller (MKD).

1. Posterior Knowledge Explorer (PoKE): This module is designed to extract explicit abnormal visual regions from radiology images using a set of predefined disease topic tags. By aligning these tags with the image features, PoKE reduces the visual data bias, allowing the model to focus on significant visual abnormalities.
2. Prior Knowledge Explorer (PrKE): Tasked with mitigating textual data bias, PrKE leverages a combination of prior medical knowledge from a knowledge graph and prior working experience accumulated from existing radiology reports. By encoding this information, PrKE helps in constructing more coherent and contextually accurate reports.
3. Multi-domain Knowledge Distiller (MKD): This component distills the relevant information from the knowledge extracted by PoKE and PrKE to generate the final report. MKD incorporates an innovative Adaptive Distilling Attention (ADA) mechanism to dynamically merge the knowledge sources based on their relevance to the particular aspects of the report being generated.

Experimental Validation and Results

The authors conducted comprehensive evaluations of the PPKED framework on two public datasets: MIMIC-CXR and IU-Xray. The results show that PPKED outperforms existing state-of-the-art models across standard metrics such as BLEU, METEOR, ROUGE-L, and CIDEr. These performance improvements can be attributed to the effective handling of data deviation issues, validating the paper's hypothesis that a balanced integration of posterior and prior knowledge can enhance report generation accuracy.

Implications and Future Developments

The significant advancements made by the PPKED framework have both theoretical and practical implications. Theoretically, it offers a robust approach to integrating heterogeneous sources of knowledge in image captioning tasks. Practically, the framework's ability to produce high-quality radiology reports can transform clinical workflows, potentially improving diagnostic accuracy and efficiency.

Looking forward, the methods introduced in this paper could have cross-disciplinary applications in other areas of medical imaging and beyond. Future research could explore the scalability of the PPKED framework to other modalities of medical images, and the potential integration of real-time feedback mechanisms from radiologists to further refine the report generation process. Additionally, examining the ethical considerations and biases related to automatic report generation remains pertinent.

In sum, this paper contributes a well-engineered method to automate the generation of comprehensive radiology reports by intelligently leveraging both posterior and prior knowledge, setting the stage for more advanced developments in medical AI applications.