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

Alopecia-Areata

富足女人

Data Augmentation from Sketchween virtual images and the intra-operative domain preserving anatomical content. Our approach has been applied to simulate intra-operative bronchoscopic videos and chest CT scans from virtual sketches generated using simple graphical primitives.

運(yùn)氣

Quantifying Uncertainty of Deep Neural Networks in Skin Lesion Classificationhe uncertainty based on the variance of the output samples. In this paper, we highlight the limitations of a variance based metric, and propose a novel uncertainty metric based on the overlap of the output distributions. We show that this yields promising results on the HAM10000 dataset for skin lesion classification.

chondromalacia

curettage

Probabilistic Surface Reconstruction with Unknown Correspondencence. To this end, we use the Metropolis-Hastings algorithm to sample reconstructions with unknown pose and correspondence from the posterior distribution. We introduce a projection-proposal to propose shape and pose updates to the Markov-Chain, which lets us explore the posterior distribution much m

isotope

HAIL

Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inferencethologies (e.g. lesions, tumours) in brain MR images. We apply the framework to two different contexts. First, we demonstrate that propagating multiple sclerosis T2 lesion segmentation results along with their associated uncertainty measures improves subsequent T2 lesion detection accuracy when eval

嬰兒

Reg R-CNN: Lesion Detection and Grading Under Noisy Labels, we propose Reg R-CNN, which replaces the second-stage classification model of a current object detector with a regression model. We show the superiority of our approach on a public data set with 1026 patients and a series of toy experiments. Code will be available at ..

Introduction

Fast Nonparametric Mutual-Information-based Registration and Uncertainty Estimationle additional code and no external tuning. Preliminary experiments on multi-modal brain MRI images show that the proposed optimizer can be both faster and more accurate than the free-form deformation method implemented in Elastix. We also demonstrate the sampler’s ability to produce direct uncertain

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