Cross-modal Memory Networks for Radiology Report Generation (2204.13258v1)

Abstract: Medical imaging plays a significant role in clinical practice of medical diagnosis, where the text reports of the images are essential in understanding them and facilitating later treatments. By generating the reports automatically, it is beneficial to help lighten the burden of radiologists and significantly promote clinical automation, which already attracts much attention in applying artificial intelligence to medical domain. Previous studies mainly follow the encoder-decoder paradigm and focus on the aspect of text generation, with few studies considering the importance of cross-modal mappings and explicitly exploit such mappings to facilitate radiology report generation. In this paper, we propose a cross-modal memory networks (CMN) to enhance the encoder-decoder framework for radiology report generation, where a shared memory is designed to record the alignment between images and texts so as to facilitate the interaction and generation across modalities. Experimental results illustrate the effectiveness of our proposed model, where state-of-the-art performance is achieved on two widely used benchmark datasets, i.e., IU X-Ray and MIMIC-CXR. Further analyses also prove that our model is able to better align information from radiology images and texts so as to help generating more accurate reports in terms of clinical indicators.

Cross-modal Memory Networks for Radiology Report Generation

The paper under review explores the task of automated radiology report generation using a novel approach that incorporates cross-modal memory networks (CMN) into the conventional encoder-decoder framework. Radiology report generation is a task that seeks to automatically produce descriptive text reports from radiology images, such as chest X-rays—a process that aligns with both NLP and computer vision domains.

Key Contributions

The primary contribution of this research is the proposal of CMN to improve the alignment and interaction between visual features extracted from radiology images and their corresponding textual descriptions. Unlike traditional models that either employ co-attention mechanisms or rely heavily on domain-specific pre-processing templates, CMN utilizes a shared memory matrix as an intermediary layer. This matrix facilitates better cross-modal mapping, thereby serving as a more integrated and effective solution to report generation.

Methodology

The CMN operates by querying and responding to a shared set of memory vectors which record cross-modal information between image features and text features. Specifically, the model extracts visual features using convolutional neural networks (CNNs), which are then aligned with textual features during both encoding and decoding phases. The memory network records these alignments, allowing the model to effectively map and translate visual regions to semantically relevant textual counterparts during the report generation process.

Results

The CMN-enhanced model demonstrates state-of-the-art performance on two benchmark datasets: IU X-Ray and MIMIC-CXR, which are prominent in radiology research. The experimental results reveal that incorporating CMN facilitates the production of more accurate reports, as measured by standard natural language generation metrics such as BLEU, METEOR, and ROUGE, as well as clinical efficacy metrics based on the CheXpert standard for thoracic diseases. Notably, the model exhibits an average improvement of 6.6% to 19.6% in NLG metrics over the baseline models without memory integration.

Implications

The implications of this research are significant for both the automation of radiology report generation and the broader intersection of NLP and computer vision. By enhancing the automated generation process, CMNs have the potential to reduce the clinical workload of radiologists, enabling more efficient diagnostic workflows. From a theoretical perspective, this work underscores the value of intermediating shared memory systems in cross-modal applications, suggesting further exploitation of such methodologies could be beneficial across various AI domains.

Future Developments

Future work could explore several dimensions:

• Scaling the CMN framework to incorporate richer contextual information and multi-modal data sources beyond text and image.
• Investigating the application of CMNs in other medical imaging tasks such as MRI or CT report generation.
• Enhancing the interpretability of the cross-modal alignments to provide more transparent insights into how the model associates image regions with language.

In summary, this work represents a methodologically sound exploration into cross-modal interactions for automated report generation, providing a pertinent foundation for subsequent advancements in the integration of textual and visual data within clinical and other domain-specific AI systems.