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Risk-Sensitive Conformal Prediction for Catheter Placement Detection in Chest X-rays

Long Hui

TL;DR

This work tackles the critical task of detecting catheter and line placements in chest X-rays by marrying a multi-task learning model—handling classification, segmentation, and landmark detection—with a risk-sensitive conformal prediction framework that delivers statistically guaranteed prediction sets. The approach yields strong uncertainty quantification, including a 99.29% coverage for critical findings and zero high-risk mispredictions, making it especially suitable for life-critical clinical contexts. The study demonstrates that uncertainty-aware tools can complement medical decision-making: while not replacing radiologists, they provide reliable guidance, triage signals, and interpretable visual explanations to support safe deployment. The combination of anatomical context, robust calibration, and an interactive interface supports practical integration into clinical workflows and highlights a path toward safer AI-assisted radiology.

Abstract

This paper presents a novel approach to catheter and line position detection in chest X-rays, combining multi-task learning with risk-sensitive conformal prediction to address critical clinical requirements. Our model simultaneously performs classification, segmentation, and landmark detection, leveraging the synergistic relationship between these tasks to improve overall performance. We further enhance clinical reliability through risk-sensitive conformal prediction, which provides statistically guaranteed prediction sets with higher reliability for clinically critical findings. Experimental results demonstrate excellent performance with 90.68\% overall empirical coverage and 99.29\% coverage for critical conditions, while maintaining remarkable precision in prediction sets. Most importantly, our risk-sensitive approach achieves zero high-risk mispredictions (cases where the system dangerously declares problematic tubes as confidently normal), making the system particularly suitable for clinical deployment. This work offers both accurate predictions and reliably quantified uncertainty -- essential features for life-critical medical applications.

Risk-Sensitive Conformal Prediction for Catheter Placement Detection in Chest X-rays

TL;DR

This work tackles the critical task of detecting catheter and line placements in chest X-rays by marrying a multi-task learning model—handling classification, segmentation, and landmark detection—with a risk-sensitive conformal prediction framework that delivers statistically guaranteed prediction sets. The approach yields strong uncertainty quantification, including a 99.29% coverage for critical findings and zero high-risk mispredictions, making it especially suitable for life-critical clinical contexts. The study demonstrates that uncertainty-aware tools can complement medical decision-making: while not replacing radiologists, they provide reliable guidance, triage signals, and interpretable visual explanations to support safe deployment. The combination of anatomical context, robust calibration, and an interactive interface supports practical integration into clinical workflows and highlights a path toward safer AI-assisted radiology.

Abstract

This paper presents a novel approach to catheter and line position detection in chest X-rays, combining multi-task learning with risk-sensitive conformal prediction to address critical clinical requirements. Our model simultaneously performs classification, segmentation, and landmark detection, leveraging the synergistic relationship between these tasks to improve overall performance. We further enhance clinical reliability through risk-sensitive conformal prediction, which provides statistically guaranteed prediction sets with higher reliability for clinically critical findings. Experimental results demonstrate excellent performance with 90.68\% overall empirical coverage and 99.29\% coverage for critical conditions, while maintaining remarkable precision in prediction sets. Most importantly, our risk-sensitive approach achieves zero high-risk mispredictions (cases where the system dangerously declares problematic tubes as confidently normal), making the system particularly suitable for clinical deployment. This work offers both accurate predictions and reliably quantified uncertainty -- essential features for life-critical medical applications.

Paper Structure

This paper contains 50 sections, 4 equations, 2 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Deep Learning in Medical Imaging
  4. Catheter and Line Detection
  5. Uncertainty Quantification and Conformal Prediction
  6. RANZCR CLiP Challenge
  7. Dataset
  8. Multi-Modal Data
  9. Segmentation Masks
  10. Trachea Bifurcation Landmark Dataset
  11. Data Preprocessing and Splitting
  12. Methodology
  13. Multi-Task Learning Framework
  14. Classification
  15. Segmentation
  16. ...and 35 more sections

Figures (2)

  • Figure 1: Multi-task learning components visualized: (a,c,e) Original chest X-rays; (b,d) Segmentation masks highlighting the exact location and path of tubes, providing spatial context for classification and visual explanation for clinicians; (f) Tracheal bifurcation landmark detection showing the carina, which serves as a critical anatomical reference for assessing ETT placement. Proper ETT positioning is typically 3-5 cm above this landmark.
  • Figure 2: The Gradio-based interactive interface with three integrated components: chest X-ray viewer with segmentation overlay toggle (left), detailed classification results and conformal prediction sets showing statistically guaranteed confidence bounds (middle), and AI-generated radiologist-style interpretation providing clinical context and recommendations (right).