A Data-Centric Framework for Intraoperative Fluorescence Lifetime Imaging for Glioma Surgical Guidance

2026-04-30 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, AI in Medical Imaging · Depth: Expert, quick

Summary

A data-centric AI (DC-AI) framework has been developed to enhance intraoperative fluorescence lifetime imaging (FLIm) for guiding glioma surgery. This framework addresses challenges like biological heterogeneity and label variability by integrating confident learning (CL), class refinement, and targeted label evaluation. Researchers collected FLIm data from 192 tissue margins across 31 IDH-wildtype glioblastoma (GBM) patients, initially labeled into seven tumor cellularity classes by an expert neuropathologist. CL was applied to quantify point-level confidence and identify label inconsistencies, leading to an iterative merging into a three-class scheme: "low", "moderate", and "high" cellularity. This refined dataset enabled training a model that achieved 96% accuracy in the three-class classification task. SHAP analysis revealed class-specific FLIm feature importance, and targeted FLIm analysis identified biological and acquisition-related factors contributing to low-confidence predictions. Blinded re-evaluation of CL-flagged margins also highlighted intra-pathologist variability, supporting selective relabeling.

Key takeaway

For AI scientists developing medical imaging tools, this DC-AI framework offers a robust approach to improve model performance in complex biological settings. You should consider integrating confident learning and iterative class refinement into your data labeling workflows, especially when dealing with high biological heterogeneity or expert label variability. This can significantly enhance data reliability and model accuracy, leading to more clinically actionable diagnostic tools.

Key insights

A data-centric AI framework improves FLIm accuracy for glioma surgery by refining labels and enhancing model robustness.

Principles

• Confident learning quantifies label reliability.
• Iterative class merging refines heterogeneous data.
• Targeted analysis identifies prediction contributors.

Method

The DC-AI framework applies confident learning to quantify FLIm point-level confidence, guides iterative class merging from seven to three classes, and uses targeted label evaluation to refine a multi-class classifier for glioblastoma resection margins.

In practice

• Use confident learning for label inconsistency detection.
• Merge classes based on data confidence for robustness.
• Employ SHAP for feature importance analysis.

Topics

• Fluorescence Lifetime Imaging
• Data-Centric AI
• Glioma Surgical Guidance
• Glioblastoma
• Confident Learning

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.