Diversity-Augmented Diffusion Network With LLM Assistance For Radiology Report Generation
| Publicatietype: | Artikel |
| Citatie: | |
| Tijdschrift: | WWW '25: Companion Proceedings of the ACM on Web Conference 2025 |
| Jaar: | 2025 |
| Maand: | Mei |
| Pagina's: | 2288-2296 |
| ISSN: | 979-8-4007-1331-6/2025/04 |
| DOI: | https://doi.org/10.1145/3701716.3717555 |
| Samenvatting: | Radiology report generation (RRG) is a demanding yet challenging task that involves producing multi-sentence diagnostic narratives, requiring long-form text with high diversity while addressing inherent data bias. Sentence-level diversity is therefore crucial for capturing varying diagnostic details across multiple regions of interest (ROIs) within a single report, yet it remains underexplored in the field. In this paper, we propose DADNET, a novel diffusion-based framework that leverages the inherent ability of diffusion models to generate diverse text. We make the first attempt to integrate large language models (LLMs) to bridge the inherent training-inference gap in diffusion models. Specifically, LLMs are used to draft a preliminary report, which is subsequently incorporated into the diffusion process to enhance report diversity. Additionally, we introduce a bias equalization technique using domain-specific priors to mitigate data distribution biases, improving the quality and reliability of generated reports under various scenarios. Experimental results demonstrate that DADNET outperforms existing approaches under the same non-autoregressive (NAR) mechanism and sets a new benchmark for diversity in RRG. This work underscores the importance of diversity in RRG and establishes DADNET as a pioneering framework for addressing this challenge with NAR methods. |
| Trefwoorden: | DiffusionModel, large language model, Radiology Report Generation |
| Auteurs | |
| Toegevoegd door: | [] |
| Totaalscore: | 0 |
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