1. Chahal, D., Byrne, M.F.: A primer on artificial intelligence and its application to endoscopy. Gastrointestinal endoscopy92(4), 813–820 (2020)

   Article  Google Scholar 

2. Chen, T., Xu, M., Hui, X., Wu, H., Lin, L.: Learning semantic-specific graph representation for multi-label image recognition. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 522–531 (2019)

   Google Scholar 

3. Fallah, H., Bruno, E., Bellot, P., Murisasco, E.: Exploiting label dependencies for multi-label document classification using transformers. In: Proceedings of the ACM Symposium on Document Engineering 2023. pp. 1–4 (2023)

   Google Scholar 

4. Gao, B.B., Zhou, H.Y.: Learning to discover multi-class attentional regions for multi-label image recognition. IEEE Transactions on Image Processing30, 5920–5932 (2021)

   Article  Google Scholar 

5. Gildenblat, J., contributors: Pytorch library for cam methods.https://github.com/jacobgil/pytorch-grad-cam (2021)

6. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)

   Google Scholar 

7. Kimura, K., Takemoto, T.: An endoscopic recognition of the atrophic border and its significance in chronic gastritis. Endoscopy1(03), 87–97 (1969)

   Article  Google Scholar 

8. Klang, E., Soroush, A., Nadkarni, G.N., Sharif, K., Lahat, A.: Deep learning and gastric cancer: Systematic review of ai-assisted endoscopy. Diagnostics13(24),  3613 (2023)

   Article  Google Scholar 

9. Lanchantin, J., Wang, T., Ordonez, V., Qi, Y.: General multi-label image classification with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16478–16488 (2021)

   Google Scholar 

10. Liu, R., Huang, J., Li, T.H., Li, G.: Causality compensated attention for contextual biased visual recognition. In: The Eleventh International Conference on Learning Representations (2022)

    Google Scholar 

11. Liu, R., Liu, H., Li, G., Hou, H., Yu, T., Yang, T.: Contextual debiasing for visual recognition with causal mechanisms. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12755–12765 (2022)

    Google Scholar 

12. Liu, S., Zhang, L., Yang, X., Su, H., Zhu, J.: Query2label: A simple transformer way to multi-label classification. arXiv preprintarXiv:2107.10834 (2021)

13. Liu, W., Wang, H., Shen, X., Tsang, I.W.: The emerging trends of multi-label learning. IEEE transactions on pattern analysis and machine intelligence44(11), 7955–7974 (2021)

    Article  Google Scholar 

14. Nega Tarekegn, A., Ullah, M., Alaya Cheikh, F.: Deep learning for multi-label learning: A comprehensive survey. arXiv e-prints pp. arXiv–2401 (2024)

    Google Scholar 

15. Pearl, J.: Causal inference in statistics: An overview (2009)

    Google Scholar 

16. Pearl, J., Glymour, M., Jewell, N.P.: Causal inference in statistics: A primer. John Wiley & Sons (2016)

    Google Scholar 

17. Tang, P., Yan, X., Nan, Y., Xiang, S., Krammer, S., Lasser, T.: Fusionm4net: A multi-stage multi-modal learning algorithm for multi-label skin lesion classification. Medical Image Analysis76, 102307 (2022)

    Article  Google Scholar 

18. Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jégou, H.: Training data-efficient image transformers & distillation through attention. In: International conference on machine learning. pp. 10347–10357. PMLR (2021)

    Google Scholar 

19. Wang, Y., He, D., Li, F., Long, X., Zhou, Z., Ma, J., Wen, S.: Multi-label classification with label graph superimposing. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 12265–12272 (2020)

    Google Scholar 

20. Wang, Z., Chen, T., Li, G., Xu, R., Lin, L.: Multi-label image recognition by recurrently discovering attentional regions. In: Proceedings of the IEEE international conference on computer vision. pp. 464–472 (2017)

    Google Scholar 

21. Yang, J., Ou, Y., Chen, Z., Liao, J., Sun, W., Luo, Y., Luo, C.: A benchmark dataset of endoscopic images and novel deep learning method to detect intestinal metaplasia and gastritis atrophy. IEEE Journal of Biomedical and Health Informatics27(1), 7–16 (2022)

    Article  Google Scholar 

22. Zhang, J., Zhao, Q., Adeli, E., Pfefferbaum, A., Sullivan, E.V., Paul, R., Valcour, V., Pohl, K.M.: Multi-label, multi-domain learning identifies compounding effects of hiv and cognitive impairment. Medical Image Analysis75, 102246 (2022)

    Article  Google Scholar 

23. Zhang, Y., Luo, L., Dou, Q., Heng, P.A.: Triplet attention and dual-pool contrastive learning for clinic-driven multi-label medical image classification. Medical Image Analysis86, 102772 (2023)

    Article  Google Scholar 

24. Zhao, H., Rai, P., Du, L., Buntine, W.: Bayesian multi-label learning with sparse features and labels, and label co-occurrences. In: International Conference on Artificial Intelligence and Statistics. pp. 1943–1951. PMLR (2018)

    Google Scholar 

25. Zhu, F., Li, H., Ouyang, W., Yu, N., Wang, X.: Learning spatial regularization with image-level supervisions for multi-label image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 5513–5522 (2017)

    Google Scholar 

26. Zhu, X., Xiong, Y., Dai, J., Yuan, L., Wei, Y.: Deep feature flow for video recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2349–2358 (2017)

    Google Scholar 

27. Zhu, X., Cao, J., Ge, J., Liu, W., Liu, B.: Two-stream transformer for multi-label image classification. In: Proceedings of the 30th ACM International Conference on Multimedia. pp. 3598–3607 (2022)

    Google Scholar 

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