Description

Book Synopsis

.- Risk management, Uncertainty interpretation and visualisation
.- MEGAN: Mixture of Experts for Robust Uncertainty Estimation in Endoscopy Videos.
.- Unsupervised Artifact Detection and Quantification via Contrastive Learning with Noise Reference.

.-Disagreement-Driven Uncertainty Quantification in Late Gadolinium
Enhancement Cardiac MRI.

.- Is Uncertainty Quantification a Viable Alternative to Learned Deferral?.
.- Evaluation of Uncertainty-Aware Multi-Software Ensembles for Hippocampal Segmentation.
.- Numerical Uncertainty in Linear Registration: An Experimental Study.
.- Domain shift and out-of-distribution management
.- SPARTA: Spectral Prompt Agnostic Adversarial Attack on Medical Vision-Language Models.
.- Label-free estimation of clinically relevant performance metrics under distribution shifts.
.- Out-of-Distribution Detection in Medical Imaging via Diffusion Trajectories.
.- SCORPION: Addressing Scanner-Induced Variability in Histopathology.
.- LEXU: Learning from Expert Disagreement for Single-Pass Uncertainty Estimation in Medical Image Segmentation.
.- Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift.
.- Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matching.
.- Uncertainty Calibration
.- Multi-Rater Calibration Error Estimation.
.- Pseudo-D: Informing Multi-View Uncertainty Estimation with Calibrated Neural Training Dynamics.
.- Metric-Guided Conformal Bounds for Probabilistic Image Reconstruction.
.- Evaluation of Monte Carlo Dropout for Uncertainty Quantification in Multi-task Deep Learning-Based Glioma Subtyping.
.- Uncertainty modelling and estimation, Bayesian deep learning
.- Benchmarking Uncertainty and its Disentanglement in multi-label Chest X-Ray Classification.
.- Uncertainty-Aware Classification: A Human-Guided Bayesian Deep Learning Framework.
.- Empirical Bayesian Methods and BNNs for Medical OOD Detection.
.- A Proper Structured Prior for Bayesian T1 Mapping.
.- Bayesian MRI Reconstruction with Structured Uncertainty Distributions.

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging

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    A Paperback by Carole H. Sudre

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      Publisher: Springer
      Publication Date: 28/10/2025
      ISBN13: 9783032065926, 978-3032065926
      ISBN10:
      Also in:
      Image processing

      Description

      Book Synopsis

      .- Risk management, Uncertainty interpretation and visualisation
      .- MEGAN: Mixture of Experts for Robust Uncertainty Estimation in Endoscopy Videos.
      .- Unsupervised Artifact Detection and Quantification via Contrastive Learning with Noise Reference.

      .-Disagreement-Driven Uncertainty Quantification in Late Gadolinium
      Enhancement Cardiac MRI.

      .- Is Uncertainty Quantification a Viable Alternative to Learned Deferral?.
      .- Evaluation of Uncertainty-Aware Multi-Software Ensembles for Hippocampal Segmentation.
      .- Numerical Uncertainty in Linear Registration: An Experimental Study.
      .- Domain shift and out-of-distribution management
      .- SPARTA: Spectral Prompt Agnostic Adversarial Attack on Medical Vision-Language Models.
      .- Label-free estimation of clinically relevant performance metrics under distribution shifts.
      .- Out-of-Distribution Detection in Medical Imaging via Diffusion Trajectories.
      .- SCORPION: Addressing Scanner-Induced Variability in Histopathology.
      .- LEXU: Learning from Expert Disagreement for Single-Pass Uncertainty Estimation in Medical Image Segmentation.
      .- Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift.
      .- Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matching.
      .- Uncertainty Calibration
      .- Multi-Rater Calibration Error Estimation.
      .- Pseudo-D: Informing Multi-View Uncertainty Estimation with Calibrated Neural Training Dynamics.
      .- Metric-Guided Conformal Bounds for Probabilistic Image Reconstruction.
      .- Evaluation of Monte Carlo Dropout for Uncertainty Quantification in Multi-task Deep Learning-Based Glioma Subtyping.
      .- Uncertainty modelling and estimation, Bayesian deep learning
      .- Benchmarking Uncertainty and its Disentanglement in multi-label Chest X-Ray Classification.
      .- Uncertainty-Aware Classification: A Human-Guided Bayesian Deep Learning Framework.
      .- Empirical Bayesian Methods and BNNs for Medical OOD Detection.
      .- A Proper Structured Prior for Bayesian T1 Mapping.
      .- Bayesian MRI Reconstruction with Structured Uncertainty Distributions.

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