Improving Chest X-Ray Report Generation by Leveraging Warm Starting (2201.09405v2)

Abstract: Automatically generating a report from a patient's Chest X-Rays (CXRs) is a promising solution to reducing clinical workload and improving patient care. However, current CXR report generators -- which are predominantly encoder-to-decoder models -- lack the diagnostic accuracy to be deployed in a clinical setting. To improve CXR report generation, we investigate warm starting the encoder and decoder with recent open-source computer vision and natural language processing checkpoints, such as the Vision Transformer (ViT) and PubMedBERT. To this end, each checkpoint is evaluated on the MIMIC-CXR and IU X-Ray datasets. Our experimental investigation demonstrates that the Convolutional vision Transformer (CvT) ImageNet-21K and the Distilled Generative Pre-trained Transformer 2 (DistilGPT2) checkpoints are best for warm starting the encoder and decoder, respectively. Compared to the state-of-the-art ($\mathcal{M}^2$ Transformer Progressive), CvT2DistilGPT2 attained an improvement of 8.3\% for CE F-1, 1.8\% for BLEU-4, 1.6\% for ROUGE-L, and 1.0\% for METEOR. The reports generated by CvT2DistilGPT2 have a higher similarity to radiologist reports than previous approaches. This indicates that leveraging warm starting improves CXR report generation. Code and checkpoints for CvT2DistilGPT2 are available at https://github.com/aehrc/cvt2distilgpt2.

Improving Chest X-Ray Report Generation by Leveraging Warm-Starting

The paper "Improving Chest X-Ray Report Generation by Leveraging Warm-Starting" introduces a multi-modal machine learning approach to enhance the generation of medical image captions, focusing on the context of chest X-rays. The authors employ transfer learning, drawing from pre-trained models within both general and medical domains, to bridge image and text representations, demonstrating enhanced performance in the task of report generation.

This research employs the integration of computer vision and NLP to address the task of caption generation from medical images, emphasizing the selection of optimal pre-trained models for initialisation. This is a pertinent decision, given the abundance of available models in repositories like Huggingface. The approach underscores the significance of combining image processing and text representation capabilities within a single neural network system to achieve superior results.

In a detailed comparative analysis, the paper aligns with recent studies in the domain of automatic medical image interpretation and diagnosis. It draws connections to works such as those by Ayesha et al. on automatic interpretation, Li et al. on multi-task contrastive learning, and Singh et al. on few-shot classification using meta-learning. These references substantiate the paper's emphasis on integrating advanced vision and NLP methodologies while distinguishing the innovative application of computer vision models in the generation of X-ray reports.

The practical implications are multifaceted: firstly, enhancing accuracy and efficiency in medical diagnostics through improved report generation; secondly, paving the way for similar applications and methodologies in non-medical domains. Theoretically, the paper contributes to the discourse on the integration of multimodal approaches in AI, aligning with broader trends towards more sophisticated, versatile models. Future developments may concentrate on refining the selection of pre-trained models and exploring additional multi-modal applications, potentially expanding the scope across various sectors requiring image-to-text translation capabilities.

Overall, this paper offers a substantial contribution to pattern recognition and its applications in medical image analysis, with potential ripple effects in adjacent fields leveraging advanced neural network structures and transfer learning techniques.