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Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration
We address the problem of uncertainty calibration and introduce a novel ...
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Semantic Segmentation of Human Thigh Quadriceps Muscle in Magnetic Resonance Images
This paper presents an end-to-end solution for MRI thigh quadriceps segm...
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VoteNet+ : An Improved Deep Learning Label Fusion Method for Multi-atlas Segmentation
In this work, we improve the performance of multi-atlas segmentation (MA...
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Verified Uncertainty Calibration
Applications such as weather forecasting and personalized medicine deman...
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Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration
Class probabilities predicted by most multiclass classifiers are uncalib...
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A Classification Refinement Strategy for Semantic Segmentation
Based on the observation that semantic segmentation errors are partially...
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Calibration with Bias-Corrected Temperature Scaling Improves Domain Adaptation Under Label Shift in Modern Neural Networks
Label shift refers to the phenomenon where the marginal probability p(y)...
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Local Temperature Scaling for Probability Calibration
For semantic segmentation, label probabilities are often uncalibrated as they are typically only the by-product of a segmentation task. Intersection over Union (IoU) and Dice score are often used as criteria for segmentation success, while metrics related to label probabilities are rarely explored. On the other hand, probability calibration approaches have been studied, which aim at matching probability outputs with experimentally observed errors, but they mainly focus on classification tasks, not on semantic segmentation. Thus, we propose a learning-based calibration method that focuses on multi-label semantic segmentation. Specifically, we adopt a tree-like convolution neural network to predict local temperature values for probability calibration. One advantage of our approach is that it does not change prediction accuracy, hence allowing for calibration as a post-processing step. Experiments on the COCO and LPBA40 datasets demonstrate improved calibration performance over different metrics. We also demonstrate the performance of our method for multi-atlas brain segmentation from magnetic resonance images.
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