Academic Journal
SegORG: Report Generation of Oral Potentially Malignant Disorders Image Based on Lesion Segmentation-Enhanced LLM.
| Title: | SegORG: Report Generation of Oral Potentially Malignant Disorders Image Based on Lesion Segmentation-Enhanced LLM. |
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| Authors: | Zhang R; Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Zhejiang Key Laboratory of Oral Biomedical, Hangzhou, China.; Life Health Innovation and Entrepreneurship Center, Institute of Wenzhou, Zhejiang University, Wenzhou, China.; Zhejiang Provincial Key Laboratory of Internet Multimedia Technology, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China., Huang P; Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Zhejiang Key Laboratory of Oral Biomedical, Hangzhou, China., Ding T; Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Zhejiang Key Laboratory of Oral Biomedical, Hangzhou, China., Chen Y; Zhejiang Provincial Key Laboratory of Internet Multimedia Technology, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China., Tian X; Zhejiang Provincial Key Laboratory of Internet Multimedia Technology, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China., Cao Y; State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China., Chen W; Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Zhejiang Key Laboratory of Oral Biomedical, Hangzhou, China., Chen X; Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Zhejiang Key Laboratory of Oral Biomedical, Hangzhou, China., Chen Q; Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Zhejiang Key Laboratory of Oral Biomedical, Hangzhou, China., Zhu F; Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Zhejiang Key Laboratory of Oral Biomedical, Hangzhou, China. |
| Source: | Oral diseases [Oral Dis] 2026 Mar; Vol. 32 (3), pp. 760-771. Date of Electronic Publication: 2025 Nov 28. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Munksgaard Country of Publication: Denmark NLM ID: 9508565 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1601-0825 (Electronic) Linking ISSN: 1354523X NLM ISO Abbreviation: Oral Dis Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2001- : Copenhagen, Denmark : Munksgaard Original Publication: Houndmills, Basingstoke, Hampshire, UK : Stockton Press, c1995- |
| MeSH Terms: | Mouth Neoplasms*/diagnostic imaging , Mouth Neoplasms*/pathology , Precancerous Conditions*/diagnostic imaging , Image Processing, Computer-Assisted*/methods , Natural Language Processing*, Mouth Mucosa/diagnostic imaging ; Mouth Mucosa/pathology ; Humans |
| Abstract: | Objectives: To develop an automated system for generating standardized reports for oral potentially malignant disorders (OPMDs) from white-light images, aiming to reduce documentation workload, facilitate early intervention, and enable longitudinal lesion monitoring. Methods: We proposed the SegORG model using 441 oral mucosa images and 1323 corresponding reports. It employed SegFormer for lesion segmentation; a visual encoder extracted global and local visual embeddings, which were projected into a pre-trained large language model (LLM feature) space via a lightweight visual mapper. The Qwen2.5-7B model then generated structured diagnostic reports, enhanced by text augmentation techniques to improve diversity and professionalism. Results: SegORG achieved BLEU-4, ROUGE-L, and CIDEr scores of 0.291, 0.517, and 0.578, respectively. Additionally, the model obtained a clinical diagnostic F1-score of 0.695 and a median expert rating of 4 on the Likert scale (p < 0.001). It significantly outperformed conventional baseline models (R2Gen, METransformer, and SwinB+BERT9k) and contemporary general-purpose multimodal LLMs (GPT-4, Gemini 2.5 Pro, and Qwen2.5-VL), despite its lean architecture (90.4 M trainable parameters). Conclusions: By enhancing visual feature extraction and achieving efficient text alignment, SegORG offers an effective technical pathway for OPMDs reports automation. While single-center validation shows promise, multicenter trials are needed to assess generalizability. (© 2025 John Wiley & Sons Ltd.) |
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| Grant Information: | 2024YFC2510700 the National Key R&D Program of China; XMGL-KJZX-202401 Science and Technology Special Project, Institute of Wenzhou, Zhejiang University; 82471034 the National Natural Science Foundation of China; 82301070 the National Natural Science Foundation of China |
| Contributed Indexing: | Keywords: large language models; lesion segmentation; medical report generation; oral potentially malignant disorders; text augmentation; vision transformer |
| Entry Date(s): | Date Created: 20251128 Date Completed: 20260429 Latest Revision: 20260429 |
| Update Code: | 20260429 |
| DOI: | 10.1111/odi.70148 |
| PMID: | 41313616 |
| Database: | MEDLINE |
| FullText | Links: – Type: other Url: https://resolver.ebsco.com:443/public/rma-ftfapi/ejs/direct?AccessToken=45B39E79D0EFCCCF3F08&Show=Object Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: SegORG: Report Generation of Oral Potentially Malignant Disorders Image Based on Lesion Segmentation-Enhanced LLM. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Zhang+R%22">Zhang R</searchLink>; Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Zhejiang Key Laboratory of Oral Biomedical, Hangzhou, China.; Life Health Innovation and Entrepreneurship Center, Institute of Wenzhou, Zhejiang University, Wenzhou, China.; Zhejiang Provincial Key Laboratory of Internet Multimedia Technology, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.<br /><searchLink fieldCode="AU" term="%22Huang+P%22">Huang P</searchLink>; Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Zhejiang Key Laboratory of Oral Biomedical, Hangzhou, China.<br /><searchLink fieldCode="AU" term="%22Ding+T%22">Ding T</searchLink>; Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Zhejiang Key Laboratory of Oral Biomedical, Hangzhou, China.<br /><searchLink fieldCode="AU" term="%22Chen+Y%22">Chen Y</searchLink>; Zhejiang Provincial Key Laboratory of Internet Multimedia Technology, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.<br /><searchLink fieldCode="AU" term="%22Tian+X%22">Tian X</searchLink>; Zhejiang Provincial Key Laboratory of Internet Multimedia Technology, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.<br /><searchLink fieldCode="AU" term="%22Cao+Y%22">Cao Y</searchLink>; State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China.<br /><searchLink fieldCode="AU" term="%22Chen+W%22">Chen W</searchLink>; Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Zhejiang Key Laboratory of Oral Biomedical, Hangzhou, China.<br /><searchLink fieldCode="AU" term="%22Chen+X%22">Chen X</searchLink>; Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Zhejiang Key Laboratory of Oral Biomedical, Hangzhou, China.<br /><searchLink fieldCode="AU" term="%22Chen+Q%22">Chen Q</searchLink>; Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Zhejiang Key Laboratory of Oral Biomedical, Hangzhou, China.<br /><searchLink fieldCode="AU" term="%22Zhu+F%22">Zhu F</searchLink>; Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Zhejiang Key Laboratory of Oral Biomedical, Hangzhou, China. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%229508565%22">Oral diseases</searchLink> [Oral Dis] 2026 Mar; Vol. 32 (3), pp. 760-771. <i>Date of Electronic Publication: </i>2025 Nov 28. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article – Name: Language Label: Language Group: Lang Data: English – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Munksgaard%22">Munksgaard </searchLink><i>Country of Publication: </i>Denmark <i>NLM ID: </i>9508565 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1601-0825 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%221354523X%22">1354523X </searchLink><i>NLM ISO Abbreviation: </i>Oral Dis <i>Subsets: </i>MEDLINE – Name: PublisherInfo Label: Imprint Name(s) Group: PubInfo Data: <i>Publication</i>: 2001- : Copenhagen, Denmark : Munksgaard<br /><i>Original Publication</i>: Houndmills, Basingstoke, Hampshire, UK : Stockton Press, c1995- – Name: SubjectMESH Label: MeSH Terms Group: Su Data: <searchLink fieldCode="MM" term="%22Mouth+Neoplasms%22">Mouth Neoplasms*</searchLink>/<searchLink fieldCode="MM" term="%22Mouth+Neoplasms+diagnostic+imaging%22">diagnostic imaging</searchLink> <br /><searchLink fieldCode="MM" term="%22Mouth+Neoplasms%22">Mouth Neoplasms*</searchLink>/<searchLink fieldCode="MM" term="%22Mouth+Neoplasms+pathology%22">pathology</searchLink> <br /><searchLink fieldCode="MM" term="%22Precancerous+Conditions%22">Precancerous Conditions*</searchLink>/<searchLink fieldCode="MM" term="%22Precancerous+Conditions+diagnostic+imaging%22">diagnostic imaging</searchLink> <br /><searchLink fieldCode="MM" term="%22Image+Processing%2C+Computer-Assisted%22">Image Processing, Computer-Assisted*</searchLink>/<searchLink fieldCode="MM" term="%22Image+Processing%2C+Computer-Assisted+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Natural+Language+Processing%22">Natural Language Processing*</searchLink><br /><searchLink fieldCode="MH" term="%22Mouth+Mucosa%22">Mouth Mucosa</searchLink>/<searchLink fieldCode="MH" term="%22Mouth+Mucosa+diagnostic+imaging%22">diagnostic imaging</searchLink> ; <searchLink fieldCode="MH" term="%22Mouth+Mucosa%22">Mouth Mucosa</searchLink>/<searchLink fieldCode="MH" term="%22Mouth+Mucosa+pathology%22">pathology</searchLink> ; <searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Objectives: To develop an automated system for generating standardized reports for oral potentially malignant disorders (OPMDs) from white-light images, aiming to reduce documentation workload, facilitate early intervention, and enable longitudinal lesion monitoring.<br />Methods: We proposed the SegORG model using 441 oral mucosa images and 1323 corresponding reports. It employed SegFormer for lesion segmentation; a visual encoder extracted global and local visual embeddings, which were projected into a pre-trained large language model (LLM feature) space via a lightweight visual mapper. The Qwen2.5-7B model then generated structured diagnostic reports, enhanced by text augmentation techniques to improve diversity and professionalism.<br />Results: SegORG achieved BLEU-4, ROUGE-L, and CIDEr scores of 0.291, 0.517, and 0.578, respectively. Additionally, the model obtained a clinical diagnostic F1-score of 0.695 and a median expert rating of 4 on the Likert scale (p &lt; 0.001). It significantly outperformed conventional baseline models (R2Gen, METransformer, and SwinB+BERT9k) and contemporary general-purpose multimodal LLMs (GPT-4, Gemini 2.5 Pro, and Qwen2.5-VL), despite its lean architecture (90.4 M trainable parameters).<br />Conclusions: By enhancing visual feature extraction and achieving efficient text alignment, SegORG offers an effective technical pathway for OPMDs reports automation. While single-center validation shows promise, multicenter trials are needed to assess generalizability.<br /> (© 2025 John Wiley & Sons Ltd.) – Name: Ref Label: References Group: RefInfo Data: Aksoy, N., N. Ravikumar, and A. F. Frangi. 2023. “Radiology Report Generation Using Transformers Conditioned With Non‐Imaging Data. Paper Presented at the Medical Imaging 2023: Imaging Informatics for Healthcare, Research, and Applications.”.<br />Chen, Z., Y. Shen, Y. Song, and X. 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