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检索条件"任意字段=5th International Conference on Medical Image Computing and Computer-Assisted Intervention"

共 2874 条记录，以下是391-400 订阅

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One-Shot Federated Learning on medical Data Using Knowledge Distillation with image Synthesis and Client Model Adaptation 26th

One-Shot Federated Learning on Medical Data Using Knowledge ...

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

作者： Kang, Myeongkyun Chikontwe, Philip Kim, Soopil Jin, Kyong Hwan Adeli, Ehsan Pohl, Kilian M. Park, Sang Hyun Daegu Gyeongbuk Inst Sci & Technol DGIST Robot & Mech Engn Daegu South Korea Daegu Gyeongbuk Inst Sci & Technol DGIST Elect Engn & Comp Sci Daegu South Korea Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9783031438943;9783031438950

One-shot federated learning (FL) has emerged as a promising solution in scenarios where multiple communication rounds are not practical. Notably, as feature distributions in medical data are less discriminative than those of natural images, robust global model training with FL is non-trivial and can lead to overfitting. To address this issue, we propose a novel one-shot FL framework leveraging image Synthesis and Client model Adaptation (FedISCA) with knowledge distillation (KD). To prevent overfitting, we generate diverse synthetic images ranging from random noise to realistic images. this approach (i) alleviates data privacy concerns and (ii) facilitates robust global model training using KD with decentralized client models. To mitigate domain disparity in the early stages of synthesis, we design noise-adapted client models where batch normalization statistics on random noise (synthetic images) are updated to enhance KD. Lastly, the global model is trained with both the original and noise-adapted client models via KD and synthetic images. this process is repeated till global model convergence. Extensive evaluation of this design on five small- and three large-scale medical image classification datasets reveals superior accuracy over prior methods. Code is available at https://***/myeongkyunkang/FedISCA.

关键词： One-Shot Federated Learning Knowledge Distillation Noise image Synthesis Client Model Adaptation

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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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Elongated Physiological Structure Segmentation via Spatial and Scale Uncertainty-Aware Network 26th

Elongated Physiological Structure Segmentation via Spatial a...

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

作者： Zhang, Yinglin Xi, Ruiling Fu, Huazhu Towey, Dave Bai, RuiBin Higashita, Risa Liu, Jiang Univ Nottingham Ningbo China Sch Comp Sci Ningbo 315100 Peoples R China Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Tomey Corp Nagoya Aichi 4510051 Japan ASTAR Inst High Performance Comp IHPC Singapore Singapore

ISBN: (纸本)9783031439001;9783031439018

Robust and accurate segmentation for elongated physiological structures is challenging, especially in the ambiguous region, such as the corneal endothelium microscope image with uneven illumination or the fundus image with disease interference. In this paper, we present a spatial and scale uncertainty-aware network (SSU-Net) that fully uses both spatial and scale uncertainty to highlight ambiguous regions and integrate hierarchical structure contexts. First, we estimate epistemic and aleatoric spatial uncertainty maps using Monte Carlo dropout to approximate Bayesian networks. Based on these spatial uncertainty maps, we propose the gated soft uncertainty-aware (GSUA) module to guide the model to focus on ambiguous regions. Second, we extract the uncertainty under different scales and propose the multiscale uncertainty-aware (MSUA) fusion module to integrate structure contexts from hierarchical predictions, strengthening the final prediction. Finally, we visualize the uncertainty map of final prediction, providing interpretability for segmentation results. Experiment results show that the SSU-Net performs best on cornea endothelial cell and retinal vessel segmentation tasks. Moreover, compared with counterpart uncertainty-based methods, SSU-Net is more accurate and robust.

关键词： Uncertainty medical image Segmentation Elongated Physiological Structure Deep Learning

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Semi-supervised Class Imbalanced Deep Learning for Cardiac MRI Segmentation 26th

Semi-supervised Class Imbalanced Deep Learning for Cardiac M...

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

作者： Yuan, Yuchen Wang, Xi Yang, Xikai Li, Ruijiang Heng, Pheng-Ann Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China Stanford Univ Sch Med Dept Radiat Oncol Stanford CA USA Chinese Univ Hong Kong Inst Med Intelligence & XR Shatin Hong Kong Peoples R China

ISBN: (纸本)9783031439001;9783031439018

Despite great progress in semi-supervised learning (SSL) that leverages unlabeled data to improve the performance over fully supervised models, existing SSL approaches still fail to exhibit good results when faced with a severe class imbalance problem in medical image segmentation. In this work, we propose a novel Mean-teacher based class imbalanced learning framework for cardiac magnetic resonance imaging (MRI) segmentation, which can effectively conquer the problems of class imbalance and limited labeled data simultaneously. Specifically, in parallel to the traditional linear-based classifier, we additionally train a prototype-based classifier that makes dense predictions by matching test samples with a set of prototypes. the prototypes are iteratively updated by in-class features encoded in the entire sample set, which can better guide the model training by alleviating the classwise bias exhibited in each individual sample. To reduce the noises in the pseudo labels, we propose a cascaded refining strategy by utilizing two multi-level tree filters that are built upon pairwise pixel similarity in terms of intensity values and semantic features. With the assistance of pixel affinities, soft pseudo labels are properly refined on-the-fly. Upon evaluation on ACDC and MMWHS, two cardiacMRI datasets with prominent class imbalance problem, the proposed method demonstrates the superiority compared to several state-of-the-art methods, especially in the case where few annotations are available (Code is available in https://***/IsYuchenYuan/SSCI).

关键词： Semi-supervised learning medical image segmentation Class imbalance Prototype learning Multi-level tree filters

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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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Quantum computing for image Processing and Artificial Vision: Innovations in Advanced Quantum Physics 5

Quantum Computing for Image Processing and Artificial Vision...

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5th international conference on Pattern Analysis and Intelligent Systems, PAIS 2023

作者： Nouioua, Tarek Yahiaoui, Nour El-Houda University of Tebessa Faculty of Exact Sciences and Sciences of Nature and Life Department of Mathematics and Computer Sciences Tebessa Algeria Photonics Optics Professional Formation Center Dris Mohamed El-Hadi Residential Formation Tebessa Algeria

ISBN: (纸本)9798350381450

this paper delves into the groundbreaking potential of quantum computing, with a primary focus on qubits and their wide-ranging applications. It explores the fundamental principles of quantum physics, such as entanglement and superposition, demonstrating how these principles underpin innovative approaches to data representation and processing. Real-world demonstrations utilizing quantum hardware underscore the exponential advantages that quantum computing offers in terms of both speed and storage capacity. Within the realm of quantum computing, one major objective is to address the issue of decoherence, and the article proposes the utilization of photons as qubits due to their virtually infinite decoherence times. this introduction of Photonic quantum computing paves the way for a robust and scalable quantum computing approach, with specific objectives centered around enhancing the stability and scalability of quantum systems. Furthermore, the article explores the fascinating domain of quantum image processing, aiming to revolutionize the fields of image analysis and compression. Quantum image representations are envisioned to significantly enhance storage efficiency, and the article sets objectives to develop practical quantum algorithms for image processing tasks. To illustrate the practicality of these advancements, the article showcases a real-world implementation of quantum edge detection, emphasizing its promising applications not only in medical imaging but also in advancing artificial vision systems. © 2023 IEEE.

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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