Leveraging Multi-Rater Annotations to Calibrate Object Detectors in Microscopy Imaging
Deep learning-based object detectors have achieved impressive performance in microscopy imaging, yet their confidence estimates often lack calibration, limiting their reliability for biomedical applications. In this work, we introduce a new approach to improve model calibration by leveraging multi-rater annotations. We propose to train separate models on the annotations from single experts and aggregate their predictions to emulate consensus. This improves upon label sampling strategies, where models are trained on mixed annotations, and offers a more principled way to capture inter-rater variability. Experiments on a colorectal organoid dataset annotated by two experts demonstrate that our rater-specific ensemble strategy improves calibration performance while maintaining comparable detection accuracy. These findings suggest that explicitly modelling rater disagreement can lead to more trustworthy object detectors in biomedical imaging.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Learning self-calibrated optic disc and cup segmentation from multi-rater annotations
The segmentation of optic disc(OD) and optic cup(OC) from fundus images is an important fundamental task for glaucoma diagnosis. In the clinical practice, it is often necessary to collect opinions from multiple experts t…
SegmentationMulti-Rater Calibrated Segmentation Models
Objective: Accurate probability estimates are essential for the safe deployment of medical image segmentation models in clinical decision-making. However, modern deep segmentation networks are often poorly calibrated, a …
Medical Image SegmentationLearning Calibrated Medical Image Segmentation via Multi-Rater Agreement Modeling
In medical image analysis, it is typical to collect multiple annotations, each from a different clinical expert or rater, in the expectation that possible diagnostic errors could be mitigated. Meanwhile, from the com…
DiagnosticImage SegmentationMedical Image AnalysisMedical Image Segmentation+2Multi-rater Prism: Learning self-calibrated medical image segmentation from multiple raters
In medical image segmentation, it is often necessary to collect opinions from multiple experts to make the final decision. This clinical routine helps to mitigate individual bias. But when data is multiply annotated, sta…
Image SegmentationMedical Image SegmentationSegmentationSemantic SegmentationLabels have Human Values: Value Calibration of Subjective Tasks
Building NLP systems for subjective tasks requires one to ensure their alignment to contrasting human values. We propose the MultiCalibrated Subjective Task Learner framework (MC-STL), which clusters annotations into ide…