GutCore was pretrained on 5.6 million de-identified endoscopic images. The study tested whether all images from one endoscopic examination could be aggregated for cancer detection, invasion-depth assessment, biomarker-associated phenotypes, and survival stratification.
Public image-level benchmarks were used as supporting evaluations. GutCore remains a retrospective research model and is not intended for clinical decision-making without further validation.
Click or drag de-identified frames to replay case-level probabilities and attention maps.
Attention analysis
Video attention replay
Heatmaps are generated from cancer-detection model attention scores over the same endoscopy clip.
Diagnosis
Non-cancer vs pathology-confirmed gastric cancer.
AUC 0.995Depth
Early vs advanced gastric cancer.
AUC 0.960Biomarkers
Tissue-defined biomarker status from whole-case images.
AUC 0.831 / 0.854Prognosis
Risk scores for overall survival in AGC.
C-index 0.681@article{kim2026gutcore,
title = {GutCore: An Endoscopy Foundation Model for Whole-Case Gastric Cancer Analysis},
author = {Kim, Sehun and Yoo, Hyosoon and Yoo, Seong-Keun and Lee, Jeeyun and Min, Yang Won and Lee, Hyuk},
journal = {medRxiv},
year = {2026},
doi = {10.64898/2026.07.01.26356993},
url = {https://www.medrxiv.org/content/10.64898/2026.07.01.26356993v1}
}