Information processing in medical imaging : proceedings of the ... conference最新文献_第4页

TetCNN: Convolutional Neural Networks on Tetrahedral Meshes 四面体网格上的卷积神经网络

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-02-08 DOI: 10.48550/arXiv.2302.03830

Mohammad Farazi, Zhangsihao Yang, Wenjie Zhu, Peijie Qiu, Yalin Wang

{"title":"TetCNN: Convolutional Neural Networks on Tetrahedral Meshes","authors":"Mohammad Farazi, Zhangsihao Yang, Wenjie Zhu, Peijie Qiu, Yalin Wang","doi":"10.48550/arXiv.2302.03830","DOIUrl":"https://doi.org/10.48550/arXiv.2302.03830","url":null,"abstract":"Convolutional neural networks (CNN) have been broadly studied on images, videos, graphs, and triangular meshes. However, it has seldom been studied on tetrahedral meshes. Given the merits of using volumetric meshes in applications like brain image analysis, we introduce a novel interpretable graph CNN framework for the tetrahedral mesh structure. Inspired by ChebyNet, our model exploits the volumetric Laplace-Beltrami Operator (LBO) to define filters over commonly used graph Laplacian which lacks the Riemannian metric information of 3D manifolds. For pooling adaptation, we introduce new objective functions for localized minimum cuts in the Graclus algorithm based on the LBO. We employ a piece-wise constant approximation scheme that uses the clustering assignment matrix to estimate the LBO on sampled meshes after each pooling. Finally, adapting the Gradient-weighted Class Activation Mapping algorithm for tetrahedral meshes, we use the obtained heatmaps to visualize discovered regions-of-interest as biomarkers. We demonstrate the effectiveness of our model on cortical tetrahedral meshes from patients with Alzheimer's disease, as there is scientific evidence showing the correlation of cortical thickness to neurodegenerative disease progression. Our results show the superiority of our LBO-based convolution layer and adapted pooling over the conventionally used unitary cortical thickness, graph Laplacian, and point cloud representation.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"19 1","pages":"303-315"},"PeriodicalIF":0.0,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83781040","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: 0

Med-NCA: Robust and Lightweight Segmentation with Neural Cellular Automata Med-NCA:基于神经细胞自动机的鲁棒轻量级分割

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-02-07 DOI: 10.48550/arXiv.2302.03473

John Kalkhof, Camila Gonz'alez, A. Mukhopadhyay

{"title":"Med-NCA: Robust and Lightweight Segmentation with Neural Cellular Automata","authors":"John Kalkhof, Camila Gonz'alez, A. Mukhopadhyay","doi":"10.48550/arXiv.2302.03473","DOIUrl":"https://doi.org/10.48550/arXiv.2302.03473","url":null,"abstract":"Access to the proper infrastructure is critical when performing medical image segmentation with Deep Learning. This requirement makes it difficult to run state-of-the-art segmentation models in resource-constrained scenarios like primary care facilities in rural areas and during crises. The recently emerging field of Neural Cellular Automata (NCA) has shown that locally interacting one-cell models can achieve competitive results in tasks such as image generation or segmentations in low-resolution inputs. However, they are constrained by high VRAM requirements and the difficulty of reaching convergence for high-resolution images. To counteract these limitations we propose Med-NCA, an end-to-end NCA training pipeline for high-resolution image segmentation. Our method follows a two-step process. Global knowledge is first communicated between cells across the downscaled image. Following that, patch-based segmentation is performed. Our proposed Med-NCA outperforms the classic UNet by 2% and 3% Dice for hippocampus and prostate segmentation, respectively, while also being 500 times smaller. We also show that Med-NCA is by design invariant with respect to image scale, shape and translation, experiencing only slight performance degradation even with strong shifts; and is robust against MRI acquisition artefacts. Med-NCA enables high-resolution medical image segmentation even on a Raspberry Pi B+, arguably the smallest device able to run PyTorch and that can be powered by a standard power bank.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"4 1","pages":"705-716"},"PeriodicalIF":0.0,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86978022","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: 1

OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing OTRE：最优传输引导的非配对图像到图像翻译通过增强满足正则化

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-02-06 DOI: 10.48550/arXiv.2302.03003

Wenjie Zhu, Peijie Qiu, O. Dumitrascu, Jacob Jacob, Mohammad Farazi, Zhangsihao Yang, Keshav Nandakumar, Yalin Wang

{"title":"OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing","authors":"Wenjie Zhu, Peijie Qiu, O. Dumitrascu, Jacob Jacob, Mohammad Farazi, Zhangsihao Yang, Keshav Nandakumar, Yalin Wang","doi":"10.48550/arXiv.2302.03003","DOIUrl":"https://doi.org/10.48550/arXiv.2302.03003","url":null,"abstract":"Non-mydriatic retinal color fundus photography (CFP) is widely available due to the advantage of not requiring pupillary dilation, however, is prone to poor quality due to operators, systemic imperfections, or patient-related causes. Optimal retinal image quality is mandated for accurate medical diagnoses and automated analyses. Herein, we leveraged the Optimal Transport (OT) theory to propose an unpaired image-to-image translation scheme for mapping low-quality retinal CFPs to high-quality counterparts. Furthermore, to improve the flexibility, robustness, and applicability of our image enhancement pipeline in the clinical practice, we generalized a state-of-the-art model-based image reconstruction method, regularization by denoising, by plugging in priors learned by our OT-guided image-to-image translation network. We named it as regularization by enhancing (RE). We validated the integrated framework, OTRE, on three publicly available retinal image datasets by assessing the quality after enhancement and their performance on various downstream tasks, including diabetic retinopathy grading, vessel segmentation, and diabetic lesion segmentation. The experimental results demonstrated the superiority of our proposed framework over some state-of-the-art unsupervised competitors and a state-of-the-art supervised method.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"13939 1","pages":"415-427"},"PeriodicalIF":0.0,"publicationDate":"2023-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45135680","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: 1

An Unsupervised Framework for Joint MRI Super Resolution and Gibbs Artifact Removal 联合MRI超分辨率和吉布斯伪影去除的无监督框架

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-02-06 DOI: 10.48550/arXiv.2302.02849

Yikang Liu, Eric Z. Chen, Xiao Chen, Terrence Chen, Shanhui Sun

{"title":"An Unsupervised Framework for Joint MRI Super Resolution and Gibbs Artifact Removal","authors":"Yikang Liu, Eric Z. Chen, Xiao Chen, Terrence Chen, Shanhui Sun","doi":"10.48550/arXiv.2302.02849","DOIUrl":"https://doi.org/10.48550/arXiv.2302.02849","url":null,"abstract":"The k-space data generated from magnetic resonance imaging (MRI) is only a finite sampling of underlying signals. Therefore, MRI images often suffer from low spatial resolution and Gibbs ringing artifacts. Previous studies tackled these two problems separately, where super resolution methods tend to enhance Gibbs artifacts, whereas Gibbs ringing removal methods tend to blur the images. It is also a challenge that high resolution ground truth is hard to obtain in clinical MRI. In this paper, we propose an unsupervised learning framework for both MRI super resolution and Gibbs artifacts removal without using high resolution ground truth. Furthermore, we propose regularization methods to improve the model's generalizability across out-of-distribution MRI images. We evaluated our proposed methods with other state-of-the-art methods on eight MRI datasets with various contrasts and anatomical structures. Our method not only achieves the best SR performance but also significantly reduces the Gibbs artifacts. Our method also demonstrates good generalizability across different datasets, which is beneficial to clinical applications where training data are usually scarce and biased.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"1 1","pages":"403-414"},"PeriodicalIF":0.0,"publicationDate":"2023-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89236240","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: 0

Harmonizing Flows: Unsupervised MR harmonization based on normalizing flows 协调流:基于规范化流的无监督MR协调

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-01-27 DOI: 10.48550/arXiv.2301.11551

Farzad Beizaee, Christian Desrosiers, G. Lodygensky, J. Dolz

引用次数: 2

On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations 基于正交表征学习的多敏感属性医学图像分类公平性研究

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-01-04 DOI: 10.48550/arXiv.2301.01481

Wenlong Deng, Yuan Zhong, Qianming Dou, Xiaoxiao Li

{"title":"On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations","authors":"Wenlong Deng, Yuan Zhong, Qianming Dou, Xiaoxiao Li","doi":"10.48550/arXiv.2301.01481","DOIUrl":"https://doi.org/10.48550/arXiv.2301.01481","url":null,"abstract":"Mitigating the discrimination of machine learning models has gained increasing attention in medical image analysis. However, rare works focus on fair treatments for patients with multiple sensitive demographic ones, which is a crucial yet challenging problem for real-world clinical applications. In this paper, we propose a novel method for fair representation learning with respect to multi-sensitive attributes. We pursue the independence between target and multi-sensitive representations by achieving orthogonality in the representation space. Concretely, we enforce the column space orthogonality by keeping target information on the complement of a low-rank sensitive space. Furthermore, in the row space, we encourage feature dimensions between target and sensitive representations to be orthogonal. The effectiveness of the proposed method is demonstrated with extensive experiments on the CheXpert dataset. To our best knowledge, this is the first work to mitigate unfairness with respect to multiple sensitive attributes in the field of medical imaging.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"2014 1","pages":"158-169"},"PeriodicalIF":0.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86517512","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: 5

MetaViT: Metabolism-Aware Vision Transformer for Differential Diagnosis of Parkinsonism with 18F-FDG PET MetaViT:代谢感知视觉转换与18F-FDG PET鉴别诊断帕金森病

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-34048-2_11

Lin Zhao, Hexin Dong, P. Wu, Jiaying Lu, Le Lu, Jingren Zhou, Tianming Liu, Li Zhang, Ling Zhang, Yuxing Tang, C. Zuo

引用次数: 2

Multi-task Multi-instance Learning for Jointly Diagnosis and Prognosis of Early-Stage Breast Invasive Carcinoma from Whole-Slide Pathological Images 基于多任务多实例学习的早期乳腺浸润癌全片病理影像联合诊断与预后研究

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-34048-2_12

Jianxin Liu, Rongjun Ge, Peng Wan, Qi Zhu, Daoqiang Zhang, Wei Shao

引用次数: 0

Source-Free Domain Adaptation for Medical Image Segmentation via Selectively Updated Mean Teacher 基于选择性更新均值教师的医学图像分割无源域自适应

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-34048-2_18

Ziqi Wen, Xinru Zhang, Chuyang Ye

引用次数: 2

Super-Resolution Reconstruction of Fetal Brain MRI with Prior Anatomical Knowledge 胎儿脑MRI的超分辨率重建与先前的解剖学知识

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-34048-2_33

Shijie Huang, Geng Chen, Kaicong Sun, Zhiming Cui, Xukun Zhang, P. Xue, Xuan Zhang, He-Xiao Zhang, Dinggang Shen

引用次数: 0