DiffRGenNet: Difference-aware Medical Report Generation

Minghao Bian, Kun Zhang, Dexin Zhao, S Kevin Zhou
Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, PMLR 301:153-166, 2026.

Abstract

Medical report generation is a critical task in healthcare, aiming to automatically pro-duce accurate diagnostic reports from medical images, thereby alleviating the burden onradiologists. However, due to the high similarity among medical images of the same anatom-ical region and the substantial variations captured from the same region across different timepoints for individual patients, capturing these differences poses a significant challenge. Wepropose a Difference-aware Report Generation Network (DiffRGenNet), which retrievessimilar reports through image search, identifies differences using the Feature Diff module,and dynamically orchestrates global and local dependencies via the FlexiRoute AggregationModule to determine the optimal routing path for each sample, selecting the most suitablereport to describe the variations and connections. Finally, by leveraging the consistencyof classification information and the discrepancy information from the diff module, DiffR-GenNet enhances the ability to learn differences in rare diseases, generating more precisereports. Experiments demonstrate that DiffRGenNet outperforms existing methods on theMIMIC-CXR and IU X-Ray datasets, confirming its effectiveness and potential.

Cite this Paper

BibTeX
@InProceedings{pmlr-v301-bian26a, title = {DiffRGenNet: Difference-aware Medical Report Generation}, author = {Bian, Minghao and Zhang, Kun and Zhao, Dexin and Zhou, S Kevin}, booktitle = {Proceedings of The 8th International Conference on Medical Imaging with Deep Learning}, pages = {153--166}, year = {2026}, editor = {Tasdizen, Tolga and Elhabian, Shireen and Summers, Ronald and Chen, Chen and Koch, Lisa and Zhuang, Yan}, volume = {301}, series = {Proceedings of Machine Learning Research}, month = {09--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v301/main/assets/bian26a/bian26a.pdf}, url = {https://proceedings.mlr.press/v301/bian26a.html}, abstract = {Medical report generation is a critical task in healthcare, aiming to automatically pro-duce accurate diagnostic reports from medical images, thereby alleviating the burden onradiologists. However, due to the high similarity among medical images of the same anatom-ical region and the substantial variations captured from the same region across different timepoints for individual patients, capturing these differences poses a significant challenge. Wepropose a Difference-aware Report Generation Network (DiffRGenNet), which retrievessimilar reports through image search, identifies differences using the Feature Diff module,and dynamically orchestrates global and local dependencies via the FlexiRoute AggregationModule to determine the optimal routing path for each sample, selecting the most suitablereport to describe the variations and connections. Finally, by leveraging the consistencyof classification information and the discrepancy information from the diff module, DiffR-GenNet enhances the ability to learn differences in rare diseases, generating more precisereports. Experiments demonstrate that DiffRGenNet outperforms existing methods on theMIMIC-CXR and IU X-Ray datasets, confirming its effectiveness and potential.} }
Endnote
%0 Conference Paper %T DiffRGenNet: Difference-aware Medical Report Generation %A Minghao Bian %A Kun Zhang %A Dexin Zhao %A S Kevin Zhou %B Proceedings of The 8th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2026 %E Tolga Tasdizen %E Shireen Elhabian %E Ronald Summers %E Chen Chen %E Lisa Koch %E Yan Zhuang %F pmlr-v301-bian26a %I PMLR %P 153--166 %U https://proceedings.mlr.press/v301/bian26a.html %V 301 %X Medical report generation is a critical task in healthcare, aiming to automatically pro-duce accurate diagnostic reports from medical images, thereby alleviating the burden onradiologists. However, due to the high similarity among medical images of the same anatom-ical region and the substantial variations captured from the same region across different timepoints for individual patients, capturing these differences poses a significant challenge. Wepropose a Difference-aware Report Generation Network (DiffRGenNet), which retrievessimilar reports through image search, identifies differences using the Feature Diff module,and dynamically orchestrates global and local dependencies via the FlexiRoute AggregationModule to determine the optimal routing path for each sample, selecting the most suitablereport to describe the variations and connections. Finally, by leveraging the consistencyof classification information and the discrepancy information from the diff module, DiffR-GenNet enhances the ability to learn differences in rare diseases, generating more precisereports. Experiments demonstrate that DiffRGenNet outperforms existing methods on theMIMIC-CXR and IU X-Ray datasets, confirming its effectiveness and potential.
APA
Bian, M., Zhang, K., Zhao, D. & Zhou, S.K.. (2026). DiffRGenNet: Difference-aware Medical Report Generation. Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 301:153-166 Available from https://proceedings.mlr.press/v301/bian26a.html.

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