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Cross-modal Memory Networks for Radiology Report Generation
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Cross-modal Memory Networks for Radiology Report Generation
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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.
Forward citations
Cited by 8 Pith papers
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Seeing What Matters: Lesion-Aware High-Resolution Patch Discovery and Fusion for Chest X-ray Report Generation
LePaX enables high-resolution chest X-ray report generation by learning to allocate resolution to diagnostically relevant regions and fusing high-res patches back into global features without increasing token count.
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RIHA: Report-Image Hierarchical Alignment for Radiology Report Generation
RIHA proposes a hierarchical alignment transformer that uses multi-scale visual and textual feature pyramids plus optimal transport to generate more accurate radiology reports from medical images.
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Enhancing Reinforcement Learning for Radiology Report Generation with Evidence-aware Rewards and Self-correcting Preference Learning
ESC-RL improves RL for radiology reports via group-wise evidence-aware rewards (GEAR) and LLM-driven self-correcting preference learning (SPL), reaching state-of-the-art on two chest X-ray datasets.
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Seeing Through Multiple Views: Parameter-Efficient Fine-Tuning via Selective Neurons for Consistent Radiology Report Generation
View-PNDF detects and selectively fine-tunes view-specific neurons for consistent multi-view chest X-ray report generation, followed by LLM consolidation of reports.
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EchoSonar-R: A Multi-View Reasoning-Enabled Model for Disease Classification and Report Generation in Echocardiography
EchoSonar-R is a multi-view VLM for echocardiography that jointly does disease classification and report generation via SFT followed by GRPO reinforcement learning, reporting accuracy gains on private and public data.
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Medical Report Generation: A Hierarchical Task Structure-Based Cross-Modal Causal Intervention Framework
HTSC-CIF applies hierarchical task decomposition and cross-modal causal intervention to generate medical reports from images while addressing domain knowledge, alignment, and bias challenges.
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Semantic Context-aware mOdality fUsion Transformer (SCOUT): A Context-Aware Multimodal Transformer for Concept-Grounded Pathology Report Generation
SCOUT achieves state-of-the-art BLEU-1 to BLEU-4 and METEOR scores on TCGA-BRCA, MICCAI REG, and HistAI by fusing local histology, slide-level context, and semantic concepts in a context-aware transformer.
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LLaMA-XR: A Novel Framework for Radiology Report Generation using LLaMA and QLoRA Fine Tuning
LLaMA-XR fine-tunes LLaMA 3.1 with QLoRA on DenseNet-121 embeddings to generate radiology reports from chest X-rays, reporting ROUGE-L of 0.433 and METEOR of 0.336 on the IU X-ray benchmark.
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