Transformer-Based Framework for Generating Informative Follow-up Chest X-Ray Summaries with Expert Guidance

Core Concepts

A transformer-based framework is proposed to generate informative textual summaries of disease progression and device changes from a pair of follow-up and baseline chest X-ray images, leveraging expert guidance to enhance the prediction of abnormality-related words.

Abstract

The paper proposes a transformer-based framework, called Expert Insight-Enhanced (EIE) Follow-up Chest X-Ray Summary Generation, to address the task of generating informative textual summaries that describe disease progression and changes in device placement from a pair of follow-up and baseline chest X-ray images. Key highlights: The majority of existing models for radiology report generation focus on reporting abnormalities in a single X-ray examination, overlooking the importance of meaningful medical entities in follow-up summary generation. To address this, the authors introduce two mechanisms to bestow expert insight to the model: Expert soft guidance: A pretrained expert disease classifier is used to provide complementary guidance on the presence level of certain diseases. Masked entity modeling (MEM) loss: An extension of the masked language modeling (MLM) loss, which directs the model's attention toward abnormality-related words. Comprehensive experiments on the MIMIC-Diff-VQA dataset demonstrate that the performance of the proposed EIE framework is competitive with or exceeds the state-of-the-art, with the combination of both mechanisms leading to further improvements. The authors also conduct a hyperparameter sensitivity study and compare the performance of hard versus soft guidance, showing the effectiveness of the soft guidance approach.

Stats

The MIMIC-Diff-VQA dataset contains 164,324 X-ray pairs and their corresponding follow-up reports. The proposed EIE-all model achieves improvements of 1.8%, 3.0%, 3.7%, and 4.3% on the BLEU metrics compared to the state-of-the-art. EIE-all boosts the performance by 6.2% on METEOR, 5.8% on ROUGEL, and 0.671 on CIDEr, a relative increase of 65.3% compared to the state-of-the-art. EIE-all also improves the accuracy on abnormality recognition, with a 2.77% increase on Acc5 and 0.47% on Acc14.

Quotes

"Majority of the efforts on automatic generation of radiology report pertain to reporting the former, but not the latter, type of findings." "To the best of the knowledge, there is only one work [4] focusing on this field." "Motivated by our observations on the significance of medical lexicon on the fidelity of summary generation, we introduce two mechanisms to bestow expert insight to our model, namely expert soft guidance and masked entity modeling loss."

Key Insights Distilled From

Expert Insight-Enhanced Follow-up Chest X-Ray Summary Generation

by Zhichuan Wan... at arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00344.pdf

Deeper Inquiries

How can the proposed framework be extended to generate summaries that also include the location of abnormalities in the chest X-ray images?

To incorporate the location of abnormalities in the chest X-ray images into the generated summaries, the proposed framework can be extended by integrating a localization module. This module would utilize techniques such as object detection or segmentation to identify and outline the specific regions in the X-ray images where abnormalities are present. By extracting this spatial information, the model can then generate summaries that not only describe the abnormalities but also provide details on their exact locations within the images. This additional layer of information can enhance the clinical utility of the generated summaries by offering radiologists a more comprehensive understanding of the findings.

What other types of expert knowledge, beyond disease classification, could be leveraged to further improve the performance of the follow-up chest X-ray summary generation task?

In addition to disease classification, several other types of expert knowledge could be leveraged to enhance the performance of follow-up chest X-ray summary generation. One key area is anatomical knowledge, which can help the model better understand the spatial relationships between different structures in the chest area. By incorporating anatomical expertise, the model can generate summaries that not only identify abnormalities but also provide context on their proximity to specific organs or structures. Furthermore, domain-specific knowledge related to radiology protocols, imaging techniques, and clinical guidelines can also be valuable in ensuring the accuracy and relevance of the generated summaries.

How could the proposed techniques be adapted to generate summaries for other types of medical imaging modalities, such as MRI or CT scans, where the task of detecting and describing changes over time is also clinically important?

The proposed techniques can be adapted to generate summaries for other medical imaging modalities, such as MRI or CT scans, by modifying the input data and model architecture to suit the characteristics of these modalities. For MRI or CT scans, which provide detailed cross-sectional images of the body, the model can be trained on image data from these modalities along with corresponding textual reports. The model architecture can be adjusted to accommodate the unique features of MRI or CT images, such as different contrast levels and anatomical structures. Additionally, the expert insight mechanisms, such as expert soft guidance and masked entity modeling, can be tailored to capture abnormalities and changes specific to MRI or CT scans. By customizing the input representations and fine-tuning the model on datasets from these modalities, the techniques can be effectively applied to generate summaries that describe disease progression or changes in device placement over time in MRI or CT imaging studies.