Information processing in medical imaging : proceedings of the ... conference最新文献_第7页

Blob Loss: Instance Imbalance Aware Loss Functions for Semantic Segmentation Blob损失:语义分割的实例不平衡感知损失函数

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2022-05-17 DOI: 10.48550/arXiv.2205.08209

F. Kofler, Suprosanna Shit, I. Ezhov, L. Fidon, Rami Al-Maskari, Hongwei Li, H. Bhatia, T. Loehr, M. Piraud, Ali Erturk, J. Kirschke, J. Peeken, Tom Kamiel Magda Vercauteren, C. Zimmer, B. Wiestler, Bjoern H Menze

{"title":"Blob Loss: Instance Imbalance Aware Loss Functions for Semantic Segmentation","authors":"F. Kofler, Suprosanna Shit, I. Ezhov, L. Fidon, Rami Al-Maskari, Hongwei Li, H. Bhatia, T. Loehr, M. Piraud, Ali Erturk, J. Kirschke, J. Peeken, Tom Kamiel Magda Vercauteren, C. Zimmer, B. Wiestler, Bjoern H Menze","doi":"10.48550/arXiv.2205.08209","DOIUrl":"https://doi.org/10.48550/arXiv.2205.08209","url":null,"abstract":"Deep convolutional neural networks (CNN) have proven to be remarkably effective in semantic segmentation tasks. Most popular loss functions were introduced targeting improved volumetric scores, such as the Dice coefficient (DSC). By design, DSC can tackle class imbalance, however, it does not recognize instance imbalance within a class. As a result, a large foreground instance can dominate minor instances and still produce a satisfactory DSC. Nevertheless, detecting tiny instances is crucial for many applications, such as disease monitoring. For example, it is imperative to locate and surveil small-scale lesions in the follow-up of multiple sclerosis patients. We propose a novel family of loss functions, emph{blob loss}, primarily aimed at maximizing instance-level detection metrics, such as F1 score and sensitivity. emph{Blob loss} is designed for semantic segmentation problems where detecting multiple instances matters. We extensively evaluate a DSC-based emph{blob loss} in five complex 3D semantic segmentation tasks featuring pronounced instance heterogeneity in terms of texture and morphology. Compared to soft Dice loss, we achieve 5% improvement for MS lesions, 3% improvement for liver tumor, and an average 2% improvement for microscopy segmentation tasks considering F1 score.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"85 1","pages":"755-767"},"PeriodicalIF":0.0,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83889020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Modeling the Shape of the Brain Connectome via Deep Neural Networks 通过深度神经网络建模大脑连接组的形状

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2022-03-06 DOI: 10.1007/978-3-031-34048-2_23

Haocheng Dai, M. Bauer, P. Fletcher, S. Joshi

引用次数: 0

A New Bidirectional Unsupervised Domain Adaptation Segmentation Framework 一种新的双向无监督域自适应分割框架

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-08-18 DOI: 10.1007/978-3-030-78191-0_38

Munan Ning, Cheng Bian, Dong Wei, Shuang Yu, Chenglang Yuan, Yaohua Wang, Yang Guo, Kai Ma, Yefeng Zheng

引用次数: 13

Segmentation with Multiple Acceptable Annotations: A Case Study of Myocardial Segmentation in Contrast Echocardiography 分割与多个可接受的注释:心肌分割对比超声心动图的一个案例研究

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-06-29 DOI: 10.1007/978-3-030-78191-0_37

Dewen Zeng, Mingqi Li, Yukun Ding, Xiaowei Xu, Qiu Xie, Ruixue Xu, Hongwen Fei, Meiping Huang, Zhuang Jian, Yiyu Shi

引用次数: 8

A Higher Order Manifold-Valued Convolutional Neural Network with Applications to Diffusion MRI Processing 高阶流形值卷积神经网络在弥散核磁共振处理中的应用

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2021-06-28 DOI: 10.1007/978-3-030-78191-0_24

Jose J. Bouza, Chun-Hao Yang, D. Vaillancourt, B. Vemuri

引用次数: 4

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