{"product_id":"uncertainty-for-safe-utilization-of-machine-learning-in-medical-imaging-9783032065926","title":"Uncertainty for Safe Utilization of Machine Learning in Medical Imaging","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003e.- Risk management, Uncertainty interpretation and visualisation\u003cbr\u003e\u003c\/strong\u003e.- MEGAN: Mixture of Experts for Robust Uncertainty Estimation in Endoscopy Videos.\u003cbr\u003e.- Unsupervised Artifact Detection and Quantification via Contrastive Learning with Noise Reference.\u003c\/p\u003e\u003cp\u003e.-Disagreement-Driven Uncertainty Quantification in Late Gadolinium\u003cbr\u003eEnhancement Cardiac MRI.\u003c\/p\u003e\u003cp\u003e.- Is Uncertainty Quantification a Viable Alternative to Learned Deferral?.\u003cbr\u003e.- Evaluation of Uncertainty-Aware Multi-Software Ensembles for Hippocampal Segmentation.\u003cbr\u003e.- Numerical Uncertainty in Linear Registration: An Experimental Study.\u003cbr\u003e\u003cstrong\u003e.- Domain shift and out-of-distribution management\u003c\/strong\u003e\u003cbr\u003e.- SPARTA: Spectral Prompt Agnostic Adversarial Attack on Medical Vision-Language Models.\u003cbr\u003e.- Label-free estimation of clinically relevant performance metrics under distribution shifts.\u003cbr\u003e.- Out-of-Distribution Detection in Medical Imaging via Diffusion Trajectories.\u003cbr\u003e.- SCORPION: Addressing Scanner-Induced Variability in Histopathology.\u003cbr\u003e.- LEXU: Learning from Expert Disagreement for Single-Pass Uncertainty Estimation in Medical Image Segmentation.\u003cbr\u003e.- Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift.\u003cbr\u003e.- Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matching.\u003cbr\u003e\u003cstrong\u003e.- Uncertainty Calibration\u003cbr\u003e\u003c\/strong\u003e.- Multi-Rater Calibration Error Estimation.\u003cbr\u003e.- Pseudo-D: Informing Multi-View Uncertainty Estimation with Calibrated Neural Training Dynamics.\u003cbr\u003e.- Metric-Guided Conformal Bounds for Probabilistic Image Reconstruction.\u003cbr\u003e.- Evaluation of Monte Carlo Dropout for Uncertainty Quantification in Multi-task Deep Learning-Based Glioma Subtyping.\u003cbr\u003e\u003cstrong\u003e.- Uncertainty modelling and estimation, Bayesian deep \u003c\/strong\u003e\u003cstrong\u003elearning\u003cbr\u003e\u003c\/strong\u003e.- Benchmarking Uncertainty and its Disentanglement in multi-label Chest X-Ray Classification.\u003cbr\u003e.- Uncertainty-Aware Classification: A Human-Guided Bayesian Deep Learning Framework.\u003cbr\u003e.- Empirical Bayesian Methods and BNNs for Medical OOD Detection.\u003cbr\u003e.- A Proper Structured Prior for Bayesian T1 Mapping.\u003cbr\u003e.- Bayesian MRI Reconstruction with Structured Uncertainty Distributions.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":53195529617751,"sku":"9783032065926","price":44.99,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/uncertainty-for-safe-utilization-of-machine-learning-in-medical-imaging-9783032065926","provider":"Book Curl","version":"1.0","type":"link"}