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

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ConTrans: Improving Transformer with Convolutional Attention for medical image Segmentation 25th

ConTrans: Improving Transformer with Convolutional Attention...

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

作者： Lin, Ailiang Xu, Jiayu Li, Jinxing Lu, Guangming Harbin Inst Technol Shenzhen Peoples R China

ISBN: (纸本)9783031164439;9783031164422

Over the past few years, convolution neural networks (CNNs) and vision transformers (ViTs) have been two dominant architectures in medical image segmentation. Although CNNs can efficiently capture local representations, they experience difficulty establishing long-distance dependencies. Comparably, ViTs achieve impressive success owing to their powerful global contexts modeling capabilities, but they may not generalize well on insufficient datasets due to the lack of inductive biases inherent to CNNs. To inherit the merits of these two different design paradigms while avoiding their respective limitations, we propose a concurrent structure termed ConTrans, which can couple detailed localization information with global contexts to the maximum extent. ConTrans consists of two parallel encoders, i.e., a Swin Transformer encoder and a CNN encoder. Specifically, the CNN encoder is progressively stacked by the novel Depthwise Attention Block (DAB), with the aim to provide the precise local features we need. Furthermore, a well-designed Spatial-Reduction-Cross-Attention (SRCA) module is embedded in the decoder to form a comprehensive fusion of these two distinct feature representations and eliminate the semantic divergence between them. this allows to obtain accurate semantic information and ensure the up-sampling features with semantic consistency in a hierarchical manner. Extensive experiments across four typical tasks show that ConTrans significantly outperforms state-of-the-art methods on ten famous benchmarks.

关键词： medical image segmentation Transformer Convolution Neural Network Cross-attention

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A General Web-Based Platform for Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/CT images 1

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3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th international conference on medical image computing and computer assisted intervention, MICCAI 2022

作者： Jiang, Hao Haimerl, Jason Gu, Xuejun Lu, Weiguo NeuralRad LLC 8517 Exelsior Dr MadisonWI53717 United States Stanford University StanfordCA94305 United States University of Texas Southwest Medical Center DallasTX75235 United States

ISBN: (数字)9783031274206

ISBN: (纸本)9783031274190

Delineation of head and neck lesions are crucial for radiation treatment planning and follow-up studies. In this paper we developed an automated segmentation method for head and neck primary and nodal gross tumor volumes (GTVp and GTVn) segmentation in positron emission tomography/computed tomography (PET/CT) provided by the MICCAI 2022 Head and Neck Tumor Segmentation Challenge (HECKTOR 2022). Our segmentation algorithm takes nnU-Net as the backbone and uses dedicated pre- and post-processing to improve the auto-segmentation performance. the pipeline described achieved DSC results of 0.77212 (GTVp 0.77485 and GTVn 0.76938) in the testing dataset of HECTOR 2022. the developed auto-segmentation method is further extensively developed to a web-based platform to permit easy access and facilitate clinical workflow. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image segmentation

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Proceedings - 24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

Proceedings - 24th IEEE International Conference on High Per...

引用

24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

ISBN: (纸本)9798350319934

the proceedings contain 317 papers. the topics discussed include: efficiently executing sparse matrix-matrix multiplication on general purpose digital single processor;matching using sufficient dimension reduction for heterogeneity causal effect estimation;GPU parallelization and optimization of a combustion simulation application;dynamic semantic modeling of structural data sources;optimized computation for determinant of multivariate polynomial matrices on GPGPU;dual memory network for medical dialogue generation;evaluating BERT on cloud-edge time series forecasting and sentiment analysis via prompt learning;a practical adversarial attack on graph neural networks by attacking single node structure;three-channel graph convolutional network;image anomaly detection with semantic- enhanced augmentation and distributional kernel;and efficient electronic transaction query model for light nodes based on optimized blockchain structure.

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BMD-GAN: Bone Mineral Density Estimation Using X-Ray image Decomposition into Projections of Bone-Segmented Quantitative Computed Tomography Using Hierarchical Learning 25th

BMD-GAN: Bone Mineral Density Estimation Using X-Ray Image D...

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

作者： Gu, Yi Otake, Yoshito Uemura, Keisuke Soufi, Mazen Takao, Masaki Sugano, Nobuhiko Sato, Yoshinobu Nara Inst Sci & Technol Grad Sch Sci & Technol Div Informat Sci Ikoma Japan Osaka Univ Dept Orthopaed Grad Sch Med Suita Japan Ehime Univ Dept Bone & Joint Surg Grad Sch Med Matsuyama Japan Osaka Univ Dept Orthopaed Med Engn Grad Sch Med Suita Japan

ISBN: (纸本)9783031164460;9783031164453

We propose a method for estimating the bone mineral density (BMD) from a plain x-ray image. Dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) provide high accuracy in diagnosing osteoporosis;however, these modalities require special equipment and scan protocols. Measuring BMD from an x-ray image provides an opportunistic screening, which is potentially useful for early diagnosis. the previous methods that directly learn the relationship between x-ray images and BMD require a large training dataset to achieve high accuracy because of large intensity variations in the x-ray images. therefore, we propose an approach using the QCT for training a generative adversarial network (GAN) and decomposing an x-ray image into a projection of bone-segmented QCT. the proposed hierarchical learning improved the robustness and accuracy of quantitatively decomposing a small-area target. the evaluation of 200 patients with osteoarthritis using the proposed method, which we named BMD-GAN, demonstrated a Pearson correlation coefficient of 0.888 between the predicted and ground truth DXA-measured BMD. Besides not requiring a large-scale training database, another advantage of our method is its extensibility to other anatomical areas, such as the vertebrae and rib bones.

关键词： Generative adversarial network (GAN) Radiography Bone mineral density (BMD)

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Accurate and Explainable image-Based Prediction Using a Lightweight Generative Model 25th

Accurate and Explainable Image-Based Prediction Using a Ligh...

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

作者： Mauri, Chiara Cerri, Stefano Puonti, Oula Muehlau, Mark Van Leemput, Koen Tech Univ Denmark Dept Hlth Technol Lyngby Denmark Harvard Med Sch Massachusetts Gen Hosp Athinoula A Martinos Ctr Biomed Imaging Charlestown MA USA Copenhagen Univ Hosp Hvidovre Danish Res Ctr Magnet Resonance Ctr Funct & Diagnost Imaging & Res Hvidovre Denmark Tech Univ Munich Dept Neurol Munich Germany Tech Univ Munich Sch Med TUM Neuroimaging Ctr Munich Germany

ISBN: (纸本)9783031164521;9783031164514

Recent years have seen a growing interest in methods for predicting a variable of interest, such as a subject's age, from individual brain scans. Although the field has focused strongly on nonlinear discriminative methods using deep learning, here we explore whether linear generative techniques can be used as practical alternatives that are easier to tune, train and interpret. the models we propose consist of (1) a causal forward model expressing the effect of variables of interest on brain morphology, and (2) a latent variable noise model, based on factor analysis, that is quick to learn and invert. In experiments estimating individuals' age and gender from the UK Biobank dataset, we demonstrate competitive prediction performance even when the number of training subjects is in the thousands - the typical scenario in many potential applications. the method is easy to use as it has only a single hyperparameter, and directly estimates interpretable spatial maps of the underlying structural changes that are driving the predictions.

关键词： Forecasting

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Denoising for Relaxing: Unsupervised Domain Adaptive Fundus image Segmentation Without Source Data 25th

Denoising for Relaxing: Unsupervised Domain Adaptive Fundus ...

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

作者： Xu, Zhe Lu, Donghuan Wang, Yixin Luo, Jie Wei, Dong Zheng, Yefeng Tong, Raymond Kai-Yu Chinese Univ Hong Kong Dept Biomed Engn Hong Kong Peoples R China Tencent Healthcare Co Jarvis Lab Shenzhen Peoples R China Stanford Univ Dept Bioengn Stanford CA USA Harvard Med Sch Brigham & Womens Hosp Boston MA USA

ISBN: (纸本)9783031164439;9783031164422

Recently, unsupervised domain adaptation (UDA) has been actively explored for multi-site fundus image segmentation with domain discrepancy. Despite relaxing the requirement of target labels, typical UDA still requires the labeled source data to achieve distribution alignment during adaptation. Unfortunately, due to privacy concerns, the vendor side often cannot provide the source data to the targeted client side in clinical practice, making the adaptation more challenging. To address this, in this work, we present a novel uncertainty-rectified denoising-for-relaxing (U-D4R) framework, aiming at completely relaxing the source data and effectively adapting the pretrained source model to the target domain. Considering the unreliable source model predictions on the target domain, we first present an adaptive class-dependent threshold strategy as the coarse denoising process to generate the pseudo labels. then, the uncertainty-rectified label soft correction is introduced for fine denoising by taking advantage of estimating the joint distribution matrix between the observed and latent labels. Extensive experiments on cross-domain fundus image segmentation showed that our approach significantly outperforms the state-of-the-art source-free methods and encouragingly achieves comparable or even better performances over the leading source-dependent methods.

关键词： Domain adaptation Label denoising Fundus image

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Test Time Transform Prediction for Open Set Histopathological image Recognition 25th

Test Time Transform Prediction for Open Set Histopathologica...

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

作者： Galdran, Adrian Hewitt, Katherine J. Laleh, Narmin Ghaffari Kather, Jakob N. Carneiro, Gustavo Gonzalez Ballester, Miguel A. Univ Pompeu Fabra Dept Informat & Commun Technol BCN Medtech Barcelona Spain Univ Hosp RWTH Aachen Dept Med 3 Aachen Germany Univ Adelaide Adelaide SA Australia Catalan Inst Res & Adv Studies ICREA Barcelona Spain

ISBN: (纸本)9783031164347;9783031164330

Tissue typology annotation in Whole Slide histological images is a complex and tedious, yet necessary task for the development of computational pathology models. We propose to address this problem by applying Open Set Recognition techniques to the task of jointly classifying tissue that belongs to a set of annotated classes, e.g. clinically relevant tissue categories, while rejecting in test time Open Set samples, i.e. images that belong to categories not present in the training set. To this end, we introduce a new approach for Open Set histopathological image recognition based on training a model to accurately identify image categories and simultaneously predict which data augmentation transform has been applied. In test time, we measure model confidence in predicting this transform, which we expect to be lower for images in the Open Set. We carry out comprehensive experiments in the context of colorectal cancer assessment from histological images, which provide evidence on the strengths of our approach to automatically identify samples from unknown categories. Code is released at https://***/agaldran/t3po.

关键词： Histopathological image analysis Open Set Recognition

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Identifying Phenotypic Concepts Discriminating Molecular Breast Cancer Sub-Types 25th

Identifying Phenotypic Concepts Discriminating Molecular Bre...

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

作者： Fuerboeck, Christoph Perkonigg, Matthias Helbich, thomas Pinker, Katja Romeo, Valeria Langs, Georg Med Univ Vienna Dept Biomed Imaging & Image Guided Therapy Computat Imaging Res Lab Vienna Austria Med Univ Vienna Div Mol & Struct Preclin Imaging Dept Biomed Imaging & Image Guided Therapy Vienna Austria Mem Sloan Kettering Canc Ctr Breast Imaging Serv Dept Radiol New York NY USA Univ Naples Federico II Dept Adv Biomed Sci Naples Italy

ISBN: (纸本)9783031164491;9783031164484

Molecular breast cancer sub-types derived from core-biopsy are central for individual outcome prediction and treatment decisions. Determining sub-types by non-invasive imaging procedures would benefit early assessment. Furthermore, identifying phenotypic traits of subtypes may inform our understanding of disease processes as we become able to monitor them longitudinally. We propose a model to learn phenotypic appearance concepts of four molecular sub-types of breast cancer. A deep neural network classification model predicts sub-types from multimodal, multi-parametric imaging data. Intermediate representations of the visual information are clustered, and clusters are scored based on testing with concept activation vectors to assess their contribution to correctly discriminating sub-types. the proposed model can predict subtypes with competitive accuracy from simultaneous F-18-FDG PET/MRI, and identifies visual traits in the form of shared and discriminating phenotypic concepts associated with the sub-types.

关键词： Explainable AI Breast cancer sub-types Deep learning

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Predicting Falls through Muscle Weakness from a Single Whole Body image: A Multimodal Contrastive Learning Framework 3rd

Predicting Falls Through Muscle Weakness from a Single Who...

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3rd international Workshop on Applications of medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Zhang, Xia Saleem, Afsah Ilyas, Zaid Suter, David Nadeem, Uzair Prince, Richard L. Zhu, Kun Lewis, Joshua R. Sim, Marc Gilani, Syed Zulqarnain Centre for AI and ML School of Science Edith Cowan University Joondalup Australia Nutrition and Health Innovation Research Institute Edith Cowan University Joondalup Australia Computer Science and Software Engineering The University of Western Australia Perth Australia Medical School The University of Western Australia Perth Australia Department of Endocrinology and Diabetes Sir Charles Gairdner Hospital PerthWA Australia

ISBN: (纸本)9783031820069

Falls are often attributed to poor muscle function, with weak hand grip strength clinically recognized as a major risk factor. However, grip strength is rarely assessed clinically. Low radiation dual-energy X-ray absorptiometry (DXA) whole-body scans, that can be obtained during routine osteoporosis screening, offer a comprehensive overview of body composition, thereby providing valuable information for musculoskeletal health. Here, we propose a machine learning technique, exploiting image and clinical data to classify weak grip strength ( © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Online Reflective Learning for Robust medical image Segmentation 25th

Online Reflective Learning for Robust Medical Image Segmenta...

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

作者： Huang, Yuhao Yang, Xin Huang, Xiaoqiong Liang, Jiamin Zhou, Xinrui Chen, Cheng Dou, Haoran Hu, Xindi Cao, Yan Ni, Dong Shenzhen Univ Sch Biomed Engn Hlth Sci Ctr Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen Peoples R China Shenzhen Univ Med Ultrasound Image Comp MUSIC Lab Shenzhen Peoples R China Shenzhen Univ Marshall Lab Biomed Engn Shenzhen Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Univ Leeds Ctr Computat Imaging & Simulat Technol Biomed CIS Leeds England Shenzhen RayShape Med Technol Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9783031164521;9783031164514

Deep segmentation models often face the failure risks when the testing image presents unseen distributions. Improving model robustness against these risks is crucial for the large-scale clinical application of deep models. In this study, inspired by human learning cycle, we propose a novel online reflective learning framework (RefSeg) to improve segmentation robustness. Based on the reflection-on-action conception, our RefSeg firstly drives the deep model to take action to obtain semantic segmentation. then, RefSeg triggers the model to reflect itself. Because making deep models realize their segmentation failures during testing is challenging, RefSeg synthesizes a realistic proxy image from the semantic mask to help deep models build intuitive and effective reflections. this proxy translates and emphasizes the segmentation flaws. By maximizing the structural similarity between the raw input and the proxy, the reflection-on-action loop is closed with segmentation robustness improved. RefSeg runs in the testing phase and is general for segmentation models. Extensive validation on three medical image segmentation tasks with a public cardiac MR dataset and two in-house large ultrasound datasets show that our RefSeg remarkably improves model robustness and reports state-of-the-art performance over strong competitors.

关键词： Segmentation Robustness Online learning

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