Information processing in medical imaging : proceedings of the ... conference最新文献_第10页

Hierarchical Morphology-Guided Tooth Instance Segmentation from CBCT Images 基于分层形态学的CBCT图像牙例分割

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-78191-0_12

Zhiming Cui, Bojun Zhang, C. Lian, Changjian Li, Lei Yang, Wenping Wang, Min Zhu, D. Shen

引用次数: 13

Semi-Supervised Screening of COVID-19 from Positive and Unlabeled Data with Constraint Non-Negative Risk Estimator 基于约束非负风险估计器的阳性和未标记数据中COVID-19的半监督筛选

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-78191-0_47

Zhongyi Han, Rundong He, Tianyang Li, B. Wei, Jian Wang, Yilong Yin

引用次数: 6

Nested Grassmanns for Dimensionality Reduction with Applications to Shape Analysis 用于降维的嵌套格拉斯曼在形状分析中的应用

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-78191-0_11

Chun-Hao Yang, B. Vemuri

引用次数: 3

Quantile Regression for Uncertainty Estimation in VAEs with Applications to Brain Lesion Detection. 分位数回归的不确定性估计及其在脑损伤检测中的应用。

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-01-01 Epub Date: 2021-06-14 DOI: 10.1007/978-3-030-78191-0_53

Haleh Akrami, Anand Joshi, Sergul Aydore, Richard Leahy

{"title":"Quantile Regression for Uncertainty Estimation in VAEs with Applications to Brain Lesion Detection.","authors":"Haleh Akrami, Anand Joshi, Sergul Aydore, Richard Leahy","doi":"10.1007/978-3-030-78191-0_53","DOIUrl":"https://doi.org/10.1007/978-3-030-78191-0_53","url":null,"abstract":"<p><p>The Variational AutoEncoder (VAE) has become one of the most popular models for anomaly detection in applications such as lesion detection in medical images. The VAE is a generative graphical model that is used to learn the data distribution from samples and then generate new samples from this distribution. By training on normal samples, the VAE can be used to detect inputs that deviate from this learned distribution. The VAE models the output as a conditionally independent Gaussian characterized by means and variances for each output dimension. VAEs can therefore use reconstruction probability instead of reconstruction error for anomaly detection. Unfortunately, joint optimization of both mean and variance in the VAE leads to the well-known problem of shrinkage or underestimation of variance. We describe an alternative VAE model, Quantile-Regression VAE (QR-VAE), that avoids this variance shrinkage problem by estimating conditional quantiles for the given input image. Using the estimated quantiles, we compute the conditional mean and variance for input images under the Gaussian model. We then compute reconstruction probability using this model as a principled approach to outlier or anomaly detection. We also show how our approach can be used for heterogeneous thresholding of images for detecting lesions in brain images.</p>","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":" ","pages":"689-700"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321392/pdf/nihms-1723560.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39264529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Velocity-To-Pressure (V2P) - Net: Inferring Relative Pressures from Time-Varying 3D Fluid Flow Velocities 速度-压力(V2P) -网络:从时变的三维流体流动速度推断相对压力

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-78191-0_42

Suprosanna Shit, Dhritiman Das, I. Ezhov, J. Paetzold, A. F. Sanches, N. Thürey, Bjoern H Menze

引用次数: 6

A Probabilistic Framework for Modeling the Variability Across Federated Datasets 跨联邦数据集可变性建模的概率框架

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-78191-0_54

Irene Balelli, R. SantiagoS.Silva, Marco Lorenzi

引用次数: 3

Mixture Modeling for Identifying Subtypes in Disease Course Mapping 混合模型在疾病病程制图中识别亚型

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-78191-0_44

Pierre-Emmanuel Poulet, S. Durrleman

引用次数: 2

Geodesic Tubes for Uncertainty Quantification in Diffusion MRI 用于扩散MRI不确定度定量的测地线管

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-78191-0_22

Rick Sengers, L. Florack, A. Fuster

引用次数: 3

Feature Library: A Benchmark for Cervical Lesion Segmentation 特征库:宫颈病变分割的基准

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-78191-0_34

Yuexiang Li, Jiawei Chen, Kai Ma, Yefeng Zheng

引用次数: 0

Weakly Supervised Deep Learning for Aortic Valve Finite Element Mesh Generation from 3D CT Images 基于弱监督深度学习的主动脉瓣三维CT图像有限元网格生成

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-78191-0_49

Daniel H. Pak, Minliang Liu, S. Ahn, A. Caballero, John A. Onofrey, L. Liang, Wei Sun, J. Duncan

引用次数: 1