APA (7th ed.) Citation

Sucipto, K., Khosla, A., Drage, M., Wang, Y., Fahy, D., Lin, M., . . . Najdawi, F. (2023). QUANTITATIVE AND EXPLAINABLE ARTIFICIAL INTELLIGENCE (AI)-POWERED APPROACHES TO PREDICT ULCERATIVE COLITIS DISEASE ACTIVITY FROM HEMATOXYLIN AND EOSIN (H&E)-STAINED WHOLE SLIDE IMAGES (WSI). Inflammatory bowel diseases, 29(Supplement_1), S22-S23. https://doi.org/10.1093/ibd/izac247.042

Chicago Style (17th ed.) Citation

Sucipto, Kathleen, et al. "QUANTITATIVE AND EXPLAINABLE ARTIFICIAL INTELLIGENCE (AI)-POWERED APPROACHES TO PREDICT ULCERATIVE COLITIS DISEASE ACTIVITY FROM HEMATOXYLIN AND EOSIN (H&E)-STAINED WHOLE SLIDE IMAGES (WSI)." Inflammatory Bowel Diseases 29, no. Supplement_1 (2023): S22-S23. https://doi.org/10.1093/ibd/izac247.042.

MLA (9th ed.) Citation

Sucipto, Kathleen, et al. "QUANTITATIVE AND EXPLAINABLE ARTIFICIAL INTELLIGENCE (AI)-POWERED APPROACHES TO PREDICT ULCERATIVE COLITIS DISEASE ACTIVITY FROM HEMATOXYLIN AND EOSIN (H&E)-STAINED WHOLE SLIDE IMAGES (WSI)." Inflammatory Bowel Diseases, vol. 29, no. Supplement_1, 2023, pp. S22-S23, https://doi.org/10.1093/ibd/izac247.042.