Cross-Modal Knowledge Diffusion-Based Generation for Difference-Aware Medical VQA.
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| Title: | Cross-Modal Knowledge Diffusion-Based Generation for Difference-Aware Medical VQA. |
|---|---|
| Authors: | Lin, Qika1 qikalin@foxmail.com, He, Kai1, Zhu, Yifan2, Xu, Fangzhi3, Cambria, Erik4, Feng, Mengling1 ephfm@nus.edu.sg |
| Source: | IEEE Transactions on Image Processing. 2025, Vol. 34, p2421-2434. 14p. |
| Subjects: | Medical assistance, Diffusion processes, Markov processes, Image processing, Signal denoising |
| Abstract: | Multimodal medical applications have garnered considerable attention due to their potential to offer comprehensive and robust support for medical assistance. Specifically, within this domain, difference-aware medical Visual Question Answering (VQA) has emerged as a topic of increasing interest that enables the recognition of changes in physical conditions over time when compared to previous states and provides customized suggestions accordingly. However, it is challenging because samples usually exhibit characteristics of complexity, diversity, and inherent noise. Besides, there is a need for multimodal knowledge understanding of the medical domain. The difference-aware setting requiring image comparison further intensifies these situations. To this end, we propose a cross-Modal knowlEdge diffusioN-baseD gEneration netwoRk (MENDER), where the diffusion mechanism with multi-step denoising and knowledge injection from global to local level are employed to tackle the aforementioned challenges, respectively. The diffusion process is to gradually generate answers with the sequence input of questions, random noises for the answer masks and virtual vision prompts of images. The strategy of answer nosing and knowledge cascading is specifically tailored for this task and is implemented during forward and reverse diffusion processes. Moreover, the visual and structure knowledge injection are proposed to learn virtual vision prompts to guide the diffusion process, where the former is realized using a pre-trained medical image-text network and the latter is modeled with spatial and semantic graph structures processed by the heterogeneous graph Transformer models. Experiment results demonstrate the effectiveness of MENDER for difference-aware medical VQA. Furthermore, it also exhibits notable performance in the low-resource setting and conventional medical VQA tasks. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Transactions on Image Processing is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: Cross-Modal Knowledge Diffusion-Based Generation for Difference-Aware Medical VQA. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lin%2C+Qika%22">Lin, Qika</searchLink><relatesTo>1</relatesTo><i> qikalin@foxmail.com</i><br /><searchLink fieldCode="AR" term="%22He%2C+Kai%22">He, Kai</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhu%2C+Yifan%22">Zhu, Yifan</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Xu%2C+Fangzhi%22">Xu, Fangzhi</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Cambria%2C+Erik%22">Cambria, Erik</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Feng%2C+Mengling%22">Feng, Mengling</searchLink><relatesTo>1</relatesTo><i> ephfm@nus.edu.sg</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Image+Processing%22">IEEE Transactions on Image Processing</searchLink>. 2025, Vol. 34, p2421-2434. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Medical+assistance%22">Medical assistance</searchLink><br /><searchLink fieldCode="DE" term="%22Diffusion+processes%22">Diffusion processes</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+denoising%22">Signal denoising</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Multimodal medical applications have garnered considerable attention due to their potential to offer comprehensive and robust support for medical assistance. Specifically, within this domain, difference-aware medical Visual Question Answering (VQA) has emerged as a topic of increasing interest that enables the recognition of changes in physical conditions over time when compared to previous states and provides customized suggestions accordingly. However, it is challenging because samples usually exhibit characteristics of complexity, diversity, and inherent noise. Besides, there is a need for multimodal knowledge understanding of the medical domain. The difference-aware setting requiring image comparison further intensifies these situations. To this end, we propose a cross-Modal knowlEdge diffusioN-baseD gEneration netwoRk (MENDER), where the diffusion mechanism with multi-step denoising and knowledge injection from global to local level are employed to tackle the aforementioned challenges, respectively. The diffusion process is to gradually generate answers with the sequence input of questions, random noises for the answer masks and virtual vision prompts of images. The strategy of answer nosing and knowledge cascading is specifically tailored for this task and is implemented during forward and reverse diffusion processes. Moreover, the visual and structure knowledge injection are proposed to learn virtual vision prompts to guide the diffusion process, where the former is realized using a pre-trained medical image-text network and the latter is modeled with spatial and semantic graph structures processed by the heterogeneous graph Transformer models. Experiment results demonstrate the effectiveness of MENDER for difference-aware medical VQA. Furthermore, it also exhibits notable performance in the low-resource setting and conventional medical VQA tasks. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Image Processing is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TIP.2025.3558446 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 2421 Subjects: – SubjectFull: Medical assistance Type: general – SubjectFull: Diffusion processes Type: general – SubjectFull: Markov processes Type: general – SubjectFull: Image processing Type: general – SubjectFull: Signal denoising Type: general Titles: – TitleFull: Cross-Modal Knowledge Diffusion-Based Generation for Difference-Aware Medical VQA. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lin, Qika – PersonEntity: Name: NameFull: He, Kai – PersonEntity: Name: NameFull: Zhu, Yifan – PersonEntity: Name: NameFull: Xu, Fangzhi – PersonEntity: Name: NameFull: Cambria, Erik – PersonEntity: Name: NameFull: Feng, Mengling IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 10577149 Numbering: – Type: volume Value: 34 Titles: – TitleFull: IEEE Transactions on Image Processing Type: main |
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