Direct quantification of epistemic and aleatoric uncertainty in 3D U-net segmentation-Reference-Cited by-同舟云学术

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Direct quantification of epistemic and aleatoric uncertainty in 3D U-net segmentation

Published:2022-06-08 Issue:03 Volume:9 Page:
ISSN:2329-4302
Container-title:Journal of Medical Imaging
language:
Short-container-title:J. Med. Imag.

Author:

Jones Craig K.¹,Wang Guoqing²,Yedavalli Vivek³,Sair Haris¹

Affiliation:

1. Johns Hopkins University, Malone Center for Engineering in Healthcare, Baltimore, Maryland

2. Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics, Baltimore, Maryland

3. Johns Hopkins Hospital, Russell H. Morgan Department of Radiology and Radiological Sciences, Baltimo

Publisher

SPIE-Intl Soc Optical Eng

Subject

Radiology, Nuclear Medicine and imaging

Reference36 articles.

1. Brain tumor segmentation with deep convolutional symmetric neural network

2. Segmentation of glioma tumors in brain using deep convolutional neural network

3. Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network

4. A hybrid neural network analysis of subtle brain volume differences in children surviving brain tumors

5. Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images

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4. Evidential Uncertainty Quantification: A Variance-Based Perspective;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

5. How Inter-rater Variability Relates to Aleatoric and Epistemic Uncertainty: A Case Study with Deep Learning-Based Paraspinal Muscle Segmentation;Uncertainty for Safe Utilization of Machine Learning in Medical Imaging;2023

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