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检索条件"任意字段=8th International Conference on Medical Image Computing and Computer-Assisted Intervention"

共 2694 条记录，以下是361-370 订阅

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Inter-slice Consistency for Unpaired Low-Dose CT Denoising Using Boosted Contrastive Learning 26th

Inter-slice Consistency for Unpaired Low-Dose CT Denoising U...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Jing, Jie Wang, Tao Yu, Hui Lu, Zexin Zhang, Yi Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Sichuan Univ Sch Cyber Sci & Engn Chengdu 610065 Peoples R China

ISBN: (纸本)9783031439063;9783031439070

the research field of low-dose computed tomography (LDCT) denoising is primarily dominated by supervised learning-based approaches, which necessitate the accurate registration of LDCT images and the corresponding NDCT images. However, since obtaining well-paired data is not always feasible in real clinical practice, unsupervised methods have become increasingly popular for LDCT denoising. One commonly used method is CycleGAN, but the training processing of CycleGAN is memory-intensive and mode collapse may occur. To address these limitations, we propose a novel unsupervised method based on boosted contrastive learning (BCL), which requires only a single generator. Furthermore, the constraints of computational power and memory capacity often force most existing approaches to focus solely on individual slices, leading to inconsistency in the results between consecutive slices. Our proposed BCL-based model integrates inter-slice features while maintaining the computational cost at an acceptable level comparable to most slice-based methods. Two modifications are introduced to the original contrastive learning method, including weight optimization for positive-negative pairs and imposing constraints on difference invariants. Experiments demonstrate that our method outperforms existing several state-of-the-art supervised and unsupervised methods in both qualitative and quantitative metrics.

关键词： Low-dose computed tomography unsupervised learning image denoising machine learning

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FedGrav: An Adaptive Federated Aggregation Algorithm for Multi-institutional medical image Segmentation 26th

FedGrav: An Adaptive Federated Aggregation Algorithm for Mul...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Deng, Zhifang Li, Dandan Tan, Shi Fu, Ying Yuan, Xueguang Huang, Xiaohong Zhang, Yong Zhou, Guangwei Beijing Univ Posts & Telecommun Sch Comp Sci Natl Pilot Software Engn Sch Beijing Peoples R China Peking Univ Third Hosp Dept Ultrasound Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Elect Engn Beijing Peoples R China Zhongguancun Lab Beijing Peoples R China HTA Co Ltd Beijing Peoples R China

ISBN: (纸本)9783031438943;9783031438950

With the increasingly strengthened data privacy acts and the difficult data centralization, Federated Learning (FL) has become an effective solution to collaboratively train the model while preserving each client's privacy. FedAvg is a standard aggregation algorithm that makes the proportion of the dataset size of each client an aggregation weight. However, it can't deal with non-independent and identically distributed (non-IID) data well because of its fixed aggregation weights and the neglect of data distribution. the paper presents a new aggregation strategy called FedGrav, which is designed to handle non-IID datasets and is inspired by the law of universal gravitation in physics. FedGrav can dynamically adjust the aggregation weights based on the training condition of local models throughout the entire training process, making it an effective solution for non-IID data. the model affinity is creatively proposed by considering both the differences of sample size on the client and the discrepancies among local models. It considers the client sample size as the mass of the local model and defines the model graph distance based on neural network topology. By calculating the affinity among local models, FedGrav can explore internal correlations of them and improve the aggregation weights. the proposed FedGrav has been applied to the CIFAR-10 and the MICCAI Federated Tumor Segmentation (FeTS) Challenge 2021 datasets, and the validation results show that our method outperforms the previous state-of-the-art by 1.54 mean DSC and 2.89 mean HD95. the source code will be available on Github.

关键词： Federated Learning Brain Tumor Segmentation FedGrav Model Affinity Graph Distance

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A Coupled-Mechanisms Modelling Framework for Neurodegeneration 26th

A Coupled-Mechanisms Modelling Framework for Neurodegenerati...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： He, Tiantian thompson, Elinor Schroder, Anna Oxtoby, Neil P. Abdulaal, Ahmed Barkhof, Frederik Alexander, Daniel C. UCL Dept Comp Sci UCL Ctr Med Image Comp London England UCL UCL Queen Sq Inst Neurol London England Vrije Univ Amsterdam Univ Med Ctr Dept Radiol & Nucl Med Amsterdam Netherlands

ISBN: (纸本)9783031439926;9783031439933

Computational models of neurodegeneration aim to emulate the evolving pattern of pathology in the brain during neurodegenerative disease, such as Alzheimer's disease. Previous studies have made specific choices on the mechanisms of pathology production and diffusion, or assume that all the subjects lie on the same disease progression trajectory. However, the complexity and heterogeneity of neurodegenerative pathology suggests that multiple mechanisms may contribute synergistically with complex interactions, meanwhile the degree of contribution of each mechanism may vary among individuals. We thus put forward a coupled-mechanisms modelling framework which non-linearly combines the network-topology-informed pathology appearance with the process of pathology spreading within a dynamic modelling system. We account for the heterogeneity of disease by fitting the model at the individual level, allowing the epicenters and rate of progression to vary among subjects. We construct a Bayesian model selection framework to account for feature importance and parameter uncertainty. this provides a combination of mechanisms that best explains the observations for each individual. With the obtained distribution of mechanism importance for each subject, we are able to identify subgroups of patients sharing similar combinations of apparent mechanisms.

关键词： Network spreading model Disease progression Bayesian model selection Variational Inference

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Segment Membranes and Nuclei from Histopathological images via Nuclei Point-Level Supervision 26th

Segment Membranes and Nuclei from Histopathological Images v...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Li, Hansheng Xu, Zhengyang Zhou, Mo Shi, Xiaoshuang Kang, Yuxin Bu, Qirong Lv, Hong Li, Ming Lin, Mingzhen Cui, Lei Feng, Jun Yang, Wentao Yang, Lin Northwest Univ Xian Peoples R China Univ Elect Sci & Technol Chengdu Peoples R China Fudan Univ Shanghai Peoples R China Shanghai Med Univ 2 Shanghai Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Accurate segmentation and analysis of membranes from immunohistochemical (IHC) images are crucial for cancer diagnosis and prognosis. Although several fully-supervised deep learning methods for membrane segmentation from IHC images have been proposed recently, the high demand for pixel-level annotations makes this process time-consuming and labor-intensive. To overcome this issue, we propose a novel deep framework for membrane segmentation that utilizes nuclei point-level supervision. Our framework consists of two networks: a Seg-Net that generates segmentation results for membranes and nuclei, and a Tran-Net that transforms the segmentation into semantic points. In this way, the accuracy of the semantic points is closely related to the segmentation quality. thus, the inconsistency between the semantic points and the point annotations can be used as effective supervision for cell segmentation. We evaluated the proposed method on two IHC membrane-stained datasets and achieved an 81.36% IoU and 85.51% F-1 score of the fully supervised method. All source codes are available here.

关键词： Membrane segmentation Point-based supervision Immunohistochemical image

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LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion 26th

LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy w...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Bai, Long Chen, Tong Wu, Yanan Wang, An Islam, Mobarakol Ren, Hongliang Chinese Univ Hong Kong CUHK Dept Elect Engn Hong Kong Peoples R China Univ Sydney Sydney NSW Australia Northeastern Univ Shenyang Peoples R China UCL Wellcome EPSRC Ctr Intervent & Surg Sci WEISS London England CUHK Shun Hing Inst Adv Engn Hong Kong Peoples R China

ISBN: (纸本)9783031439988;9783031439995

Wireless capsule endoscopy (WCE) is a painless and non-invasive diagnostic tool for gastrointestinal (GI) diseases. However, due to GI anatomical constraints and hardware manufacturing limitations, WCE vision signals may suffer from insufficient illumination, leading to a complicated screening and examination procedure. Deep learning-based low-light image enhancement (LLIE) in the medical field gradually attracts researchers. Given the exuberant development of the denoising diffusion probabilistic model (DDPM) in computer vision, we introduce a WCE LLIE framework based on the multi-scale convolutional neural network (CNN) and reverse diffusion process. the multi-scale design allows models to preserve high-resolution representation and context information from low-resolution, while the curved wavelet attention (CWA) block is proposed for high-frequency and local feature learning. Moreover, we combine the reverse diffusion procedure to optimize the shallow output further and generate images highly approximate to real ones. the proposed method is compared with eleven state-of-the-art (SOTA) LLIE methods and significantly outperforms quantitatively and qualitatively. the superior performance on GI disease segmentation further demonstrates the clinical potential of our proposed model. Our code is publicly accessible at ***/longbai1006/LLCaps.

关键词： image enhancement

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Semantic Difference Guidance for the Uncertain Boundary Segmentation of CT Left Atrial Appendage 26th

Semantic Difference Guidance for the Uncertain Boundary Segm...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： You, Xin Ding, Ming Zhang, Minghui Wu, Yangqian Yu, Yi Gu, Yun Yang, Jie Shanghai Jiao Tong Univ Inst Med Robot Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Xinhua Hosp Shanghai Peoples R China

ISBN: (纸本)9783031439896;9783031439902

Atrial fibrillation (AF) is one of the most common types of cardiac arrhythmia, which is closely relevant to anatomical structures including the left atrium (LA) and the left atrial appendage (LAA). thus, a thorough understanding of the LA and LAA is essential for the AF treatment. In this paper, we have modeled relative relations between the LA and LAA via deep segmentation networks for the first time, and introduce a new LA & LAA CT dataset. To deal with uncertain boundaries between the LA and LAA, we propose the semantic difference module (SDM) based on diffusion theory to refine features with enhanced boundary information. Besides, disconnections between the LA and LAA are frequently observed in the segmentation results due to uncertain boundaries of the LAA region and CT imaging noise. To address this issue, we devise another connectivity-refined network with the connectivity loss. the loss function exerts a distance regularization on coarse predictions from the first-stage network. Experiments demonstrate that our proposed model can achieve state-of-the-art segmentation performance compared with classic convolutional-neural-networks (CNNs) and recent Transformer-based models on this new dataset. Specifically, SDM can also outperform existing methods on refining uncertain boundaries. Codes are available at https://***/AlexYouXin/LA- LAA-segmentation.

关键词： Left atrial appendage Difference operator Uncertain boundary image segmentation

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Anatomy-Informed Data Augmentation for Enhanced Prostate Cancer Detection 26th

Anatomy-Informed Data Augmentation for Enhanced Prostate Can...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Kovacs, Balint Netzer, Nils Baumgartner, Michael Eith, Carolin Bounias, Dimitrios Meinzer, Clara Jaeger, Paul F. Zhang, Kevin S. Floca, Ralf Schrader, Adrian Isensee, Fabian Gnirs, Regula Goertz, Magdalena Schuetz, Viktoria Stenzinger, Albrecht Hohenfellner, Markus Schlemmer, Heinz-Peter Wolf, Ivo Bonekamp, David Maier-Hein, Klaus H. German Canc Res Ctr Div Med Image Comp Heidelberg Germany German Canc Res Ctr Div Radiol Heidelberg Germany Heidelberg Univ Med Fac Heidelberg Heidelberg Germany Heidelberg Univ Fac Math & Comp Sci Heidelberg Germany German Canc Res Ctr Helmholtz Imaging Heidelberg Germany German Canc Res Ctr Interact Machine Learning Grp Heidelberg Germany Heidelberg Univ Dept Urol Med Ctr Heidelberg Germany German Canc Res Ctr Jr Clin Cooperat Unit Multiparametr Methods Early Heidelberg Germany Heidelberg Univ Inst Pathol Med Ctr Heidelberg Germany Mannheim Univ Appl Sci Mannheim Germany Heidelberg Univ Hosp Dept Radiat Oncol Pattern Anal & Learning Grp Heidelberg Germany

ISBN: (纸本)9783031439896;9783031439902

Data augmentation (DA) is a key factor in medical image analysis, such as in prostate cancer (PCa) detection on magnetic resonance images. State-of-the-art computer-aided diagnosis systems still rely on simplistic spatial transformations to preserve the pathological label post transformation. However, such augmentations do not substantially increase the organ as well as tumor shape variability in the training set, limiting the model's ability to generalize to unseen cases with more diverse localized soft-tissue deformations. We propose a new anatomy-informed transformation that leverages information from adjacent organs to simulate typical physiological deformations of the prostate and generates unique lesion shapes without altering their label. Due to its lightweight computational requirements, it can be easily integrated into common DA frameworks. We demonstrate the effectiveness of our augmentation on a dataset of 774 biopsy-confirmed examinations, by evaluating a state-of-the-art method for PCa detection with different augmentation settings.

关键词： Data augmentation Soft-tissue deformation Prostate cancer detection

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A Sense of Direction in Biomedical Neural Networks 25th

A Sense of Direction in Biomedical Neural Networks

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Liu, Zewen Cootes, Timothy F. Univ Manchester Div Informat Imaging & Data Sci Stopford Bldg Manchester M13 9PT England

ISBN: (纸本)9783031164439;9783031164422

We describe an approach to making a model be aware of not only intensity but also properties such as feature direction and scale. Such properties can be important when analysing images containing curvilinear structures such as vessels or fibres. We propose the General Multi-Angle Scale Convolution (G-MASC), whose kernels are arbitrarily rotatable and also fully differentiable. the model manages its directional detectors in sets, and supervises a set's rotation symmetricity with a novel rotation penalty called PoRE. the algorithm works on pyramid representations to enable scale search. Direction and scale can be extracted from the output maps, encoded and analysed separately. Tests were conducted on three public datasets, MoNuSeg, DRIVE, and CHASE-DB1. Good performance is observed while the model requires 1% or fewer parameters compared to other approaches such as U-Net.

关键词： Steerable filter Intepretability image Segmentation

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Deployment of image Analysis Algorithms Under Prevalence Shifts 1

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Godau, Patrick Kalinowski, Piotr Christodoulou, Evangelia Reinke, Annika Tizabi, Minu Ferrer, Luciana Jaeger, Paul F. Maier-Hein, Lena German Canc Res Ctr Div Intelligent Med Syst IMSY Heidelberg Germany Natl Ctr Tumor Dis NCT Partnership DKFZ & Univ Med Ctr Heidelberg NCT Heidelberg Heidelberg Germany Heidelberg Univ Fac Math & Comp Sci Heidelberg Germany Karlsruhe Heidelberg HIDSS4Hlth Helmholtz Informat & Data Sci Sch Hlth Karlsruhe Germany German Canc Res Ctr Helmholtz Imaging Heidelberg Germany UBA Inst Ciencias Comp CONICET Buenos Aires DF Argentina German Canc Res Ctr Interact Machine Learning Grp Heidelberg Germany Heidelberg Univ Med Fac Heidelberg Germany

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Domain gaps are among the most relevant roadblocks in the clinical translation of machine learning (ML)-based solutions for medical image analysis. While current research focuses on new training paradigms and network architectures, little attention is given to the specific effect of prevalence shifts on an algorithm deployed in practice. Such discrepancies between class frequencies in the data used for a method's development/validation and that in its deployment environment(s) are of great importance, for example in the context of artificial intelligence (AI) democratization, as disease prevalences may vary widely across time and location. Our contribution is twofold. First, we empirically demonstrate the potentially severe consequences of missing prevalence handling by analyzing (i) the extent of miscalibration, (ii) the deviation of the decision threshold from the optimum, and (iii) the ability of validation metrics to reflect neural network performance on the deployment population as a function of the discrepancy between development and deployment prevalence. Second, we propose a workflow for prevalence-aware image classification that uses estimated deployment prevalences to adjust a trained classifier to a new environment, without requiring additional annotated deployment data. Comprehensive experiments based on a diverse set of 30 medical classification tasks showcase the benefit of the proposed workflow in generating better classifier decisions and more reliable performance estimates compared to current practice.

关键词： Prevalence shift medical image classification Generalization Domain Gap

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SHISRCNet: Super-Resolution and Classification Network for Low-Resolution Breast Cancer Histopathology image 26th

SHISRCNet: Super-Resolution and Classification Network for L...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Xie, Luyuan Li, Cong Wang, Zirui Zhang, Xin Chen, Boyan Shen, Qingni Wu, Zhonghai Peking Univ Sch Software & Microelect Beijing Peoples R China Peking Univ Natl Engn Res Ctr Software Engn Beijing 100871 Peoples R China Tencent Cloud Media Shenzhen Peoples R China

ISBN: (纸本)9783031439032;9783031439049

the rapid identification and accurate diagnosis of breast cancer, known as the killer of women, have become greatly significant for those patients. Numerous breast cancer histopathological image classification methods have been proposed. But they still suffer from two problems. (1) these methods can only hand high-resolution (HR) images. However, the low-resolution (LR) images are often collected by the digital slide scanner with limited hardware conditions. Compared with HR images, LR images often lose some key features like texture, which deeply affects the accuracy of diagnosis. (2) the existing methods have fixed receptive fields, so they can not extract and fuse multi-scale features well for images with different magnification factors. To fill these gaps, we present a Single Histopathological image Super-Resolution Classification network (SHISRCNet), which consists of two modules: Super-Resolution (SR) and Classification (CF) modules. SR module reconstructs LR images into SR ones. CF module extracts and fuses the multi-scale features of SR images for classification. In the training stage, we introduce HR images into the CF module to enhance SHIS-RCNet's performance. Finally, through the joint training of these two modules, super-resolution and classified of LR images are integrated into our model. the experimental results demonstrate that the effects of our method are close to the SOTA methods with taking HR images as inputs.

关键词： breast cancer histopathological image super-resolution classification joint training

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