Recognition of esophagitis in endoscopic images using transfer learning

Figure 5 Heatmaps for spatial predictive relevance in esophagitis endoscopic images from test set. A and B: Images A1-A4 represent examples esophagitis endoscopic images used to test our predictive model, while images B1-B4 represent the corresponding heatmaps indicating, for each image, the areas with the greatest influence on the prediction. A1-A4: Citation: Pogorelov K, Randel K, Griwodz C, Eskeland S, Lange T, Johansen D, Spampinato C, Dang-Nguyen D, Lux M., Schmidt P, Riegler M, Halvorsen P. Kvasir: A Multi-Class Image Dataset for Computer Aided Gastrointestinal Disease Detection. MMSys'17 Proceedings of the 8th ACM on Multimedia Systems Conference (MMSYS); 2017 June 20-23; Taipei, Taiwan. New York: Association for Computing Machinery, 2017: 164-169. Copyright © Simula Research Laboratory 2017. Published by Association for Computing Machinery[12]. The authors have obtained the permission for figure using from the Simula Research Laboratory (Supplementary material). Citation: Borgli H, Thambawita V, Smedsrud PH, Hicks S, Jha D, Eskeland SL, Randel KR, Pogorelov K, Lux M, Nguyen DTD, Johansen D, Griwodz C, Stensland HK, Garcia-Ceja E, Schmidt PT, Hammer HL, Riegler MA, Halvorsen P, de Lange T. HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy. Sci Data 2020; 7: 283. Copyright © Simula Research Laboratory 2020. Published by Nature Publishing Group[13]. The authors have obtained the permission for figure using from the Simula Research Laboratory (Supplementary material).