TwinTrack: Post-hoc Multi-Rater Calibration for Medical Image Segmentation
Summary
TwinTrack is a post-hoc calibration framework for pancreatic cancer segmentation that aligns ensemble model probabilities with the empirical mean human response across multiple annotators, improving interpretability and calibration metrics on multi-rater benchmarks.
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Paper page - TwinTrack: Post-hoc Multi-Rater Calibration for Medical Image Segmentation
Source: https://huggingface.co/papers/2604.15950
Abstract
TwinTrack framework addresses pancreatic cancer segmentation ambiguity through post-hoc calibration of ensemble probabilities to empirical mean human response, improving calibration metrics on multi-rater benchmarks.
Pancreatic ductal adenocarcinoma (PDAC) segmentation on contrast-enhanced CT is inherently ambiguous:inter-rater disagreementamong experts reflects genuine uncertainty rather than annotation noise. Standard deep learning approaches assume a single ground truth, producingprobabilistic outputsthat can be poorly calibrated and difficult to interpret under such ambiguity. We present TwinTrack, a framework that addresses this gap throughpost-hoc calibrationofensemble segmentationprobabilities to theempirical mean human response(MHR) -the fraction of expert annotators labeling a voxel as tumor. Calibrated probabilities are thus directly interpretable as the expected proportion of annotators assigning the tumor label, explicitly modelinginter-rater disagreement. The proposedpost-hoc calibrationprocedure is simple and requires only a small multi-rater calibration set. It consistently improvescalibration metricsover standard approaches when evaluated on the MICCAI 2025 CURVAS-PDACVI multi-rater benchmark.
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