Information processing in medical imaging : proceedings of the ... conference最新文献_第8页

Discovering Spreading Pathways of Neuropathological Events in Alzheimer's Disease Using Harmonic Wavelets 利用谐波小波发现阿尔茨海默病神经病理事件的传播途径

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-06-28 DOI: 10.1007/978-3-030-78191-0_18

Jiazhou Chen, Defu Yang, Hongmin Cai, M. Styner, Guorong Wu

引用次数: 2

Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition 持续主动学习使机器学习模型有效适应不断变化的图像采集

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-06-07 DOI: 10.1007/978-3-030-78191-0_50

Matthias Perkonigg, J. Hofmanninger, G. Langs

引用次数: 8

Representation Disentanglement for Multi-modal Brain MRI Analysis. 多模态脑MRI分析的表征解缠。

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-06-01 Epub Date: 2021-06-14 DOI: 10.1007/978-3-030-78191-0_25

Jiahong Ouyang, Ehsan Adeli, Kilian M Pohl, Qingyu Zhao, Greg Zaharchuk

{"title":"Representation Disentanglement for Multi-modal Brain MRI Analysis.","authors":"Jiahong Ouyang, Ehsan Adeli, Kilian M Pohl, Qingyu Zhao, Greg Zaharchuk","doi":"10.1007/978-3-030-78191-0_25","DOIUrl":"https://doi.org/10.1007/978-3-030-78191-0_25","url":null,"abstract":"Multi-modal MRIs are widely used in neuroimaging applications since different MR sequences provide complementary information about brain structures. Recent works have suggested that multi-modal deep learning analysis can benefit from explicitly disentangling anatomical (shape) and modality (appearance) information into separate image presentations. In this work, we challenge mainstream strategies by showing that they do not naturally lead to representation disentanglement both in theory and in practice. To address this issue, we propose a margin loss that regularizes the similarity in relationships of the representations across subjects and modalities. To enable robust training, we further use a conditional convolution to design a single model for encoding images of all modalities. Lastly, we propose a fusion function to combine the disentangled anatomical representations as a set of modality-invariant features for downstream tasks. We evaluate the proposed method on three multi-modal neuroimaging datasets. Experiments show that our proposed method can achieve superior disentangled representations compared to existing disentanglement strategies. Results also indicate that the fused anatomical representation has potential in the downstream task of zero-dose PET reconstruction and brain tumor segmentation.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":" ","pages":"321-333"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8844656/pdf/nihms-1776957.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39929535","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: 22

Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models. 超越显著性地图：训练深度模型，解读深度模型。

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-06-01 Epub Date: 2021-06-14 DOI: 10.1007/978-3-030-78191-0_6

Zixuan Liu, Ehsan Adeli, Kilian M Pohl, Qingyu Zhao

{"title":"Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models.","authors":"Zixuan Liu, Ehsan Adeli, Kilian M Pohl, Qingyu Zhao","doi":"10.1007/978-3-030-78191-0_6","DOIUrl":"10.1007/978-3-030-78191-0_6","url":null,"abstract":"Interpretability is a critical factor in applying complex deep learning models to advance the understanding of brain disorders in neuroimaging studies. To interpret the decision process of a trained classifier, existing techniques typically rely on saliency maps to quantify the voxel-wise or feature-level importance for classification through partial derivatives. Despite providing some level of localization, these maps are not human-understandable from the neuroscience perspective as they often do not inform the specific type of morphological changes linked to the brain disorder. Inspired by the image-to-image translation scheme, we propose to train simulator networks to inject (or remove) patterns of the disease into a given MRI based on a warping operation, such that the classifier increases (or decreases) its confidence in labeling the simulated MRI as diseased. To increase the robustness of training, we propose to couple the two simulators into a unified model based on conditional convolution. We applied our approach to interpreting classifiers trained on a synthetic dataset and two neuroimaging datasets to visualize the effect of Alzheimer's disease and alcohol dependence. Compared to the saliency maps generated by baseline approaches, our simulations and visualizations based on the Jacobian determinants of the warping field reveal meaningful and understandable patterns related to the diseases.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":" ","pages":"71-82"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8451265/pdf/nihms-1738816.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39436817","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: 0

Equivariant Spherical Deconvolution: Learning Sparse Orientation Distribution Functions from Spherical Data. 等变球面反卷积:从球面数据中学习稀疏方向分布函数。

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-06-01 DOI: 10.1007/978-3-030-78191-0_21

Axel Elaldi, Neel Dey, Heejong Kim, Guido Gerig

{"title":"Equivariant Spherical Deconvolution: Learning Sparse Orientation Distribution Functions from Spherical Data.","authors":"Axel Elaldi, Neel Dey, Heejong Kim, Guido Gerig","doi":"10.1007/978-3-030-78191-0_21","DOIUrl":"https://doi.org/10.1007/978-3-030-78191-0_21","url":null,"abstract":"We present a rotation-equivariant self-supervised learning framework for the sparse deconvolution of non-negative scalar fields on the unit sphere. Spherical signals with multiple peaks naturally arise in Diffusion MRI (dMRI), where each voxel consists of one or more signal sources corresponding to anisotropic tissue structure such as white matter. Due to spatial and spectral partial voluming, clinically-feasible dMRI struggles to resolve crossing-fiber white matter configurations, leading to extensive development in spherical deconvolution methodology to recover underlying fiber directions. However, these methods are typically linear and struggle with small crossing-angles and partial volume fraction estimation. In this work, we improve on current methodologies by nonlinearly estimating fiber structures via self-supervised spherical convolutional networks with guaranteed equivariance to spherical rotation. We perform validation via extensive single and multi-shell synthetic benchmarks demonstrating competitive performance against common base-lines. We further show improved downstream performance on fiber tractography measures on the Tractometer benchmark dataset. Finally, we show downstream improvements in terms of tractography and partial volume estimation on a multi-shell dataset of human subjects.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"12729 ","pages":"267-278"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422024/pdf/nihms-1922058.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10008226","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: 10

Cortical Morphometry Analysis based on Worst Transportation Theory. 基于最坏运输理论的皮质形态计量学分析。

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-06-01 Epub Date: 2021-06-14 DOI: 10.1007/978-3-030-78191-0_13

Min Zhang, Dongsheng An, Na Lei, Jianfeng Wu, Tong Zhao, Xiaoyin Xu, Yalin Wang, Xianfeng Gu

{"title":"Cortical Morphometry Analysis based on Worst Transportation Theory.","authors":"Min Zhang, Dongsheng An, Na Lei, Jianfeng Wu, Tong Zhao, Xiaoyin Xu, Yalin Wang, Xianfeng Gu","doi":"10.1007/978-3-030-78191-0_13","DOIUrl":"https://doi.org/10.1007/978-3-030-78191-0_13","url":null,"abstract":"Biomarkers play an important role in early detection and intervention in Alzheimer's disease (AD). However, obtaining effective biomarkers for AD is still a big challenge. In this work, we propose to use the worst transportation cost as a univariate biomarker to index cortical morphometry for tracking AD progression. The worst transportation (WT) aims to find the least economical way to transport one measure to the other, which contrasts to the optimal transportation (OT) that finds the most economical way between measures. To compute the WT cost, we generalize the Brenier theorem for the OT map to the WT map, and show that the WT map is the gradient of a concave function satisfying the Monge-Ampere equation. We also develop an efficient algorithm to compute the WT map based on computational geometry. We apply the algorithm to analyze cortical shape difference between dementia due to AD and normal aging individuals. The experimental results reveal the effectiveness of our proposed method which yields better statistical performance than other competiting methods including the OT.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":" ","pages":"163-176"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8291742/pdf/nihms-1689887.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39206848","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: 0

A Multi-scale Spatial and Temporal Attention Network on Dynamic Connectivity to Localize the Eloquent Cortex in Brain Tumor Patients 基于动态连通性的多尺度时空注意网络定位脑肿瘤患者大脑皮层

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-06-01 DOI: 10.1007/978-3-030-78191-0_19

Naresh Nandakumar, K. Manzoor, S. Agarwal, J. Pillai, S. Gujar, H. Sair, A. Venkataraman

引用次数: 4

Cytoarchitecture Measurements in Brain Gray Matter Using Likelihood-Free Inference 使用无似然推断的脑灰质细胞结构测量

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-05-15 DOI: 10.1007/978-3-030-78191-0_15

Maëliss Jallais, P. Rodrigues, Alexandre Gramfort, D. Wassermann

引用次数: 2

TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer TopoTxR:预测乳腺癌治疗反应的拓扑生物标志物

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-05-13 DOI: 10.1007/978-3-030-78191-0_30

Fan Wang, S. Kapse, Steven Liu, P. Prasanna, Chao Chen

引用次数: 10

Spatially Varying Label Smoothing: Capturing Uncertainty from Expert Annotations 空间变化标签平滑:从专家注释中捕获不确定性

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-04-12 DOI: 10.1007/978-3-030-78191-0_52

Mobarakol Islam, B. Glocker

引用次数: 26