Copyright: ©Author(s) 2026.
World J Gastroenterol. Apr 21, 2026; 32(15): 116679
Published online Apr 21, 2026. doi: 10.3748/wjg.v32.i15.116679
Published online Apr 21, 2026. doi: 10.3748/wjg.v32.i15.116679
Figure 5 Saliency map.
A: Predicted non-reversal hematoxylin and eosin image (left), its corresponding saliency map (center), and the overlaid composite image (right). Darker red regions indicate more prominent features associated with non-reversal; B: Predicted non-reversal Masson-stained image (left), its corresponding saliency map (center), and the overlaid composite image (right). Darker red regions indicate more prominent features associated with non-reversal. HE: Hematoxylin and eosin.
- Citation: Han W, Cheng DY, He QW, Wang SH, Gong SJ, Chen Y, Yang YP. Deep learning-based multimodal model for predicting on-treatment histological outcomes in chronic hepatitis B-associated advanced liver fibrosis. World J Gastroenterol 2026; 32(15): 116679
- URL: https://www.wjgnet.com/1007-9327/full/v32/i15/116679.htm
- DOI: https://dx.doi.org/10.3748/wjg.v32.i15.116679