Statistical Management of the False Discovery Rate in Medical Instance Segmentation Based on Conformal Risk Control

• URL: http://arxiv.org/abs/2504.04482v2
• Date: Sun, 27 Apr 2025 15:17:37 GMT
• Title: Statistical Management of the False Discovery Rate in Medical Instance Segmentation Based on Conformal Risk Control
• Authors: Mengxia Dai, Wenqian Luo, Tianyang Li,
• Abstract summary: Instance segmentation plays a pivotal role in medical image analysis by enabling precise localization and delineation of lesions, tumors, and anatomical structures.<n>Deep learning models such as Mask R-CNN and BlendMask have achieved remarkable progress, but their application in high-risk medical scenarios remains constrained by confidence calibration issues.<n>We propose a robust quality control framework based on conformal prediction theory to address this challenge.
• Score: 2.4578723416255754
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Instance segmentation plays a pivotal role in medical image analysis by enabling precise localization and delineation of lesions, tumors, and anatomical structures. Although deep learning models such as Mask R-CNN and BlendMask have achieved remarkable progress, their application in high-risk medical scenarios remains constrained by confidence calibration issues, which may lead to misdiagnosis. To address this challenge, we propose a robust quality control framework based on conformal prediction theory. This framework innovatively constructs a risk-aware dynamic threshold mechanism that adaptively adjusts segmentation decision boundaries according to clinical requirements.Specifically, we design a \textbf{calibration-aware loss function} that dynamically tunes the segmentation threshold based on a user-defined risk level $\alpha$. Utilizing exchangeable calibration data, this method ensures that the expected FNR or FDR on test data remains below $\alpha$ with high probability. The framework maintains compatibility with mainstream segmentation models (e.g., Mask R-CNN, BlendMask+ResNet-50-FPN) and datasets (PASCAL VOC format) without requiring architectural modifications. Empirical results demonstrate that we rigorously bound the FDR metric marginally over the test set via our developed calibration framework.

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