Automatic Radiology Report Generation based on Multi-view Image Fusion and Medical Concept Enrichment (1907.09085v2)

Abstract: Generating radiology reports is time-consuming and requires extensive expertise in practice. Therefore, reliable automatic radiology report generation is highly desired to alleviate the workload. Although deep learning techniques have been successfully applied to image classification and image captioning tasks, radiology report generation remains challenging in regards to understanding and linking complicated medical visual contents with accurate natural language descriptions. In addition, the data scales of open-access datasets that contain paired medical images and reports remain very limited. To cope with these practical challenges, we propose a generative encoder-decoder model and focus on chest x-ray images and reports with the following improvements. First, we pretrain the encoder with a large number of chest x-ray images to accurately recognize 14 common radiographic observations, while taking advantage of the multi-view images by enforcing the cross-view consistency. Second, we synthesize multi-view visual features based on a sentence-level attention mechanism in a late fusion fashion. In addition, in order to enrich the decoder with descriptive semantics and enforce the correctness of the deterministic medical-related contents such as mentions of organs or diagnoses, we extract medical concepts based on the radiology reports in the training data and fine-tune the encoder to extract the most frequent medical concepts from the x-ray images. Such concepts are fused with each decoding step by a word-level attention model. The experimental results conducted on the Indiana University Chest X-Ray dataset demonstrate that the proposed model achieves the state-of-the-art performance compared with other baseline approaches.

Overview of Automatic Radiology Report Generation

The paper "Automatic Radiology Report Generation based on Multi-view Image Fusion and Medical Concept Enrichment" explores a sophisticated approach to automate the generation of radiology reports from chest x-ray images using advanced deep learning techniques. The authors address the challenge of generating comprehensive medical reports by integrating image data with natural language processing, aiming to reduce the workload of radiologists and increase the efficiency and accuracy of medical diagnoses.

The central contribution of this research is the development of a novel generative encoder-decoder model specifically tailored for the task of radiology report generation. The model is designed to handle the complexities inherent in medical image interpretation, particularly by leveraging multi-view images and enhancing semantic understanding through medical concept enrichment.

Methodology and Model Architecture

The proposed methodology incorporates several innovative elements:

1. Encoder Pretraining with Domain-specific Data: The encoder, based on a Resnet-152 architecture, is pretrained with the CheXpert dataset—a large-scale collection of chest x-ray images—to learn radiology-specific features. This approach diverges from previous methods that relied on ImageNet-pretrained models, thus improving domain-specific feature extraction.
2. Multi-view Image Fusion: The model takes advantage of both frontal and lateral x-ray views. Unlike methods treating these views independently, this model employs a cross-view consistency loss to ensure coherent interpretations across different angles. A sentence-level attention mechanism within a late-fusion scheme is used to synthesize multi-view features, effectively integrating complementary information from different perspectives.
3. Hierarchical Decoder with Medical Concept Enrichment: A hierarchical LSTM decoder structure is employed to generate reports paragraph by paragraph. The decoder is enhanced with semantic content by incorporating frequent medical concepts extracted from training data, ensuring the generation of medically accurate and relevant reports. This is achieved through a word-level attention model that fuses medical concepts with each decoding step.

Experimental Results and Evaluation

The model's performance is rigorously evaluated using the Indiana University Chest X-Ray dataset. The proposed approach achieves state-of-the-art results in radiology report generation when benchmarked against existing methods such as visual attention-based models and knowledge-driven report generation models.

Key metrics including BLEU, METEOR, and ROUGE scores indicate significant improvements over baseline approaches, highlighting the efficacy of multi-view feature fusion and medical concept integration. Additionally, the model's ability to generate reports with high precision, particularly in medical-related content, underscores its potential practical application in clinical settings.

Implications and Future Perspectives

This research provides a meaningful advancement in the automation of radiology report generation, suggesting several implications:

• Clinical Efficiency and Error Reduction: The model aims to enhance clinical efficiency by generating preliminary reports that can assist radiologists, potentially reducing diagnostic turnaround time and minimizing human error.
• Enhanced Decision Support: By accurately identifying and focusing on uncertain radiographic observations, the model supports radiologists in prioritizing and verifying complex cases, thereby aiding in improved diagnosis and patient care.

Future research could focus on expanding the dataset to include more diverse medical images and reports, improving the model's generalization to various clinical settings. Additionally, integrating unsupervised techniques for learning from unpaired textual data could further refine the natural language generation capabilities of the model.

In conclusion, this paper lays a valuable foundation for the automation of radiology reporting, combining multi-view image analysis with enriched semantic understanding to achieve superior performance and practical utility in healthcare applications.