Memory-based Cross-modal Semantic Alignment Network for Radiology Report Generation

• URL: http://arxiv.org/abs/2404.00588v1
• Date: Sun, 31 Mar 2024 07:30:41 GMT
• Title: Memory-based Cross-modal Semantic Alignment Network for Radiology Report Generation
• Abstract summary: Key information related to disease accounts for a small proportion in both image and report. It is hard for the model to learn the latent relation between the radiology image and its report, thus failing to generate fluent and accurate radiology reports. We propose a memory-based cross-modal alignment model (MCSAM) following an encoder-decoder paradigm.
• Score: 5.314122066634083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating radiology reports automatically reduces the workload of radiologists and helps the diagnoses of specific diseases. Many existing methods take this task as modality transfer process. However, since the key information related to disease accounts for a small proportion in both image and report, it is hard for the model to learn the latent relation between the radiology image and its report, thus failing to generate fluent and accurate radiology reports. To tackle this problem, we propose a memory-based cross-modal semantic alignment model (MCSAM) following an encoder-decoder paradigm. MCSAM includes a well initialized long-term clinical memory bank to learn disease-related representations as well as prior knowledge for different modalities to retrieve and use the retrieved memory to perform feature consolidation. To ensure the semantic consistency of the retrieved cross modal prior knowledge, a cross-modal semantic alignment module (SAM) is proposed. SAM is also able to generate semantic visual feature embeddings which can be added to the decoder and benefits report generation. More importantly, to memorize the state and additional information while generating reports with the decoder, we use learnable memory tokens which can be seen as prompts. Extensive experiments demonstrate the promising performance of our proposed method which generates state-of-the-art performance on the MIMIC-CXR dataset.

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