Foundation models enable weakly supervised Nancy Index scoring for ulcerative colitis

Researchers have developed a weakly supervised multiple instance learning approach for automated scoring of ulcerative colitis activity using foundation models. This method leverages case- and slide-level labels to predict the five-grade Nancy histological index, addressing the time-consuming and variable nature of manual grading. The study evaluated various foundation models on a multicenter dataset, finding that Virchow2 performed well and that ensembling improved prediction accuracy. AI

影响 Automates histology scoring for ulcerative colitis, potentially improving clinical trial efficiency and diagnostic consistency.

排序理由 Academic paper published on arXiv detailing a new weakly supervised method for medical image analysis.

在 arXiv cs.CV 阅读 →

• arXiv
• Foundation Models
• H&E
• Ulcerative Colitis
• Virchow2
• Nancy Index

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