On the Automatic Generation of Medical Imaging Reports (1711.08195v3)

Abstract: Medical imaging is widely used in clinical practice for diagnosis and treatment. Report-writing can be error-prone for unexperienced physicians, and time- consuming and tedious for experienced physicians. To address these issues, we study the automatic generation of medical imaging reports. This task presents several challenges. First, a complete report contains multiple heterogeneous forms of information, including findings and tags. Second, abnormal regions in medical images are difficult to identify. Third, the re- ports are typically long, containing multiple sentences. To cope with these challenges, we (1) build a multi-task learning framework which jointly performs the pre- diction of tags and the generation of para- graphs, (2) propose a co-attention mechanism to localize regions containing abnormalities and generate narrations for them, (3) develop a hierarchical LSTM model to generate long paragraphs. We demonstrate the effectiveness of the proposed methods on two publicly available datasets.

On the Automatic Generation of Medical Imaging Reports

The paper by Jing, Xie, and Xing presents a comprehensive paper on the automatic generation of medical imaging reports, addressing significant challenges faced by both inexperienced and experienced radiologists. The aim is to streamline the report generation process, reducing errors and saving time in clinical practices, particularly in writing reports for complex medical images such as radiology and pathology images.

Core Contributions

The authors introduce a multi-task learning framework capable of simultaneously predicting report tags and generating text descriptions. This framework tackles the intrinsic complexity in medical reports, which often include heterogeneous information like impressions, findings, and tags.

1. Multi-Task Learning Framework: The framework integrates tasks of multi-label tag prediction and long-paragraph text generation. Tags represent critical diagnostic information, and the text provides detailed descriptions, thus covering a broad spectrum of report content.
2. Co-Attention Mechanism: A novel co-attention mechanism is proposed to improve the localization of abnormalities within an image. This approach leverages synergistic effects between visual and semantic information, enhancing the precision in identifying and narrating abnormal regions.
3. Hierarchical LSTM Model: To handle the generation of lengthy paragraphs, the authors adopt a hierarchical Long Short-Term Memory (LSTM) architecture. This hierarchical structure effectively models long sequences by separating the generation of high-level topics from the fine-grained descriptive text, ensuring coherent and contextually accurate outputs.

Experimental Validation

The framework is evaluated on two publicly available datasets: the IU X-Ray dataset and the PEIR Gross dataset. These datasets provide a robust testing ground, consisting of chest x-ray images and related radiology reports, as well as images with single-sentence descriptions. The model's performance is measured using metrics like BLEU, METEOR, ROUGE, and CIDEr.

Notably, the model's co-attention mechanism effectively captures abnormalities and generates contextually pertinent reports, outperforming several benchmark models. This result demonstrates the utility of combining visual and semantic attentions in medical image analysis.

Implications and Future Directions

Practically, this research could significantly alleviate the workload of radiologists by automating the initial report drafting process, allowing them to focus more on critical diagnostic decisions. Theoretically, the integration of multi-task learning and co-attention strategies helps bridge vision and language tasks, demonstrating a potential framework for future AI developments beyond medical imaging.

Looking forward, enhancements could involve refining the co-attention mechanism for better accuracy in other medical domains, or incorporating more sophisticated models like Transformers to further enrich text generation capabilities. The exploration of deployment in clinical settings could also provide real-world feedback, driving improvements in medical imaging AI.

By introducing a robust, multi-faceted framework, the paper advances the options available for artificial intelligence applications in medicine, particularly aiding in the efficacious interpretation and documentation of medical images.