Titlebook: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Pro; First International Hayit Greenspan,

民俗學(xué)

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書目名稱Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Pro讀者反饋




書目名稱Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Pro讀者反饋學(xué)科排名

conduct

Probabilistic Image Registration via Deep Multi-class Classification: Characterizing Uncertaintye use a deep multi-class classifier trained on different classes of patch pairs, including ., ., and a collection of discrete displacements between patches. The displacement classes alleviate the need for registration-time optimization by gradient descent; instead, posterior probabilities are used t

Medicaid

Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inferencehallenges to traditional networks. Given that medical image analysis typically requires a sequence of inference tasks to be performed (e.g. registration, segmentation), this results in an accumulation of errors over the sequence of deterministic outputs. In this paper, we explore the premise that, b

Microaneurysm

Reg R-CNN: Lesion Detection and Grading Under Noisy Labelsrrent state-of-the-art object detectors are comprised of two stages: the first stage generates region proposals, the second stage subsequently categorizes them. Unlike in natural images, however, for anatomical structures of interest such as tumors, the appearance in the image (e.g., scale or intens

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Modicum

Quantifying Uncertainty of Deep Neural Networks in Skin Lesion Classificationn in the context of medical diagnosis. However, when using a neural network as a decision support tool, it is important to also quantify the (un)certainty regarding the outputs of the system. Current Bayesian techniques approximate the true predictive output distribution via sampling, and quantify t

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A Generalized Approach to Determine Confident Samples for Deep Neural Networks on Unseen Datarformance over traditional machine learning models. However, like any other data-driven models, DNN models still face generalization limitations. For example, a model trained on clinical data from one hospital may not perform as well on data from another hospital. In this work, a novel approach is p

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liposuction