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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221-230 订阅

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Unified Brain MR-Ultrasound Synthesis Using Multi-modal Hierarchical Representations 26th

Unified Brain MR-Ultrasound Synthesis Using Multi-modal Hier...

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

作者： Dorent, Reuben Haouchine, Nazim Kogl, Fryderyk Joutard, Samuel Juvekar, Parikshit Torio, Erickson Golby, Alexandra J. Ourselin, Sebastien Frisken, Sarah Vercauteren, Tom Kapur, Tina Wells, William M., III Harvard Med Sch Brigham & Womens Hosp Boston MA 02115 USA Kings Coll London London England MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9783031439988;9783031439995

We introduce MHVAE, a deep hierarchical variational auto-encoder (VAE) that synthesizes missing images from various modalities. Extending multi-modal VAEs with a hierarchical latent structure, we introduce a probabilistic formulation for fusing multi-modal images in a common latent representation while having the flexibility to handle incomplete image sets as input. Moreover, adversarial learning is employed to generate sharper images. Extensive experiments are performed on the challenging problem of joint intra-operative ultrasound (iUS) and Magnetic Resonance (MR) synthesis. Our model outperformed multi-modal VAEs, conditional GANs, and the current state-of-the-art unified method (ResViT) for synthesizing missing images, demonstrating the advantage of using a hierarchical latent representation and a principled probabilistic fusion operation. Our code is publicly available (https://***/ReubenDo/MHVAE).

关键词： Variational Auto-Encoder Ultrasound Brain Resection image Synthesis

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ECL: Class-Enhancement Contrastive Learning for Long-Tailed Skin Lesion Classification 26th

ECL: Class-Enhancement Contrastive Learning for Long-Tailed ...

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

作者： Zhang, Yilan Chen, Jianqi Wang, Ke Xie, Fengying Beihang Univ Sch Astronaut Image Proc Ctr Beijing 100191 Peoples R China

ISBN: (纸本)9783031438943;9783031438950

Skin image datasets often suffer from imbalanced data distribution, exacerbating the difficulty of computer-aided skin disease diagnosis. Some recent works exploit supervised contrastive learning (SCL) for this long-tailed challenge. Despite achieving significant performance, these SCL-based methods focus more on head classes, yet ignoring the utilization of information in tail classes. In this paper, we propose class-Enhancement Contrastive Learning (ECL), which enriches the information of minority classes and treats different classes equally. For information enhancement, we design a hybrid-proxy model to generate class-dependent proxies and propose a cycle update strategy for parameters optimization. A balanced-hybrid-proxy loss is designed to exploit relations between samples and proxies with different classes treated equally. Taking both "imbalanced data" and "imbalanced diagnosis difficulty" into account, we further present a balanced-weighted cross-entropy loss following curriculum learning schedule. Experimental results on the classification of imbalanced skin lesion data have demonstrated the superiority and effectiveness of our method. the codes can be publicly available from https://***/zylbuaa/***.

关键词： Contrastive learning Dermoscopic image Long-tailed classification

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Minimal-Supervised medical image Segmentation via Vector Quantization Memory 1

引用

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

作者： Xu, Yanyu Zhou, Menghan Feng, Yangqin Xu, Xinxing Fu, Huazhu Goh, Rick Siow Mong Liu, Yong ASTAR Inst High Performance Comp IHPC 1 Fusionopolis Way16-16 Connexis Singapore 138632 Singapore

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

medical imaging segmentation is a critical key task for computer-assisted diagnosis and disease monitoring. However, collecting a large-scale medical dataset with well-annotation is time-consuming and requires domain knowledge. Reducing the number of annotations poses two challenges: obtaining sufficient supervision and generating high-quality pseudo labels. To address these, we propose a universal framework for annotation-efficient medical segmentation, which is capable of handling both scribble-supervised and point-supervised segmentation. Our approach includes an auxiliary reconstruction branch that provides more supervision and backwards sufficient gradients for learning visual representations. Besides, a novel pseudo label generation branch utilizes the Vector Quantization (VQ) bank to store texture-oriented and global features for generating pseudo labels. To boost the model training, we generate the high-quality pseudo labels by mixing the segmentation prediction and pseudo labels from the VQ bank. the experimental results on the ACDC MRI segmentation dataset demonstrate effectiveness of our designed method. We obtain a comparable performance (0.86 vs. 0.87 DSC score) with a few points.

关键词： Annotation-efficient Learning Vector Quantization

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Geometry-Invariant Abnormality Detection 26th

Geometry-Invariant Abnormality Detection

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

作者： Patel, Ashay Tudosiu, Petru-Daniel Pinaya, Walter Hugo Lopez Adeleke, Olusola Cook, Gary Goh, Vicky Ourselin, Sebastien Cardoso, M. Jorge Kings Coll London London WC2R 2LS England

ISBN: (纸本)9783031439063;9783031439070

Cancer is a highly heterogeneous condition best visualised in positron emission tomography. Due to this heterogeneity, a general-purpose cancer detection model can be built using unsupervised learning anomaly detection models. While prior work in this field has showcased the efficacy of abnormality detection methods (e.g. Transformer-based), these have shown significant vulnerabilities to differences in data geometry. Changes in image resolution or observed field of view can result in inaccurate predictions, even with significant data pre-processing and augmentation. We propose a new spatial conditioning mechanism that enables models to adapt and learn from varying data geometries, and apply it to a state-of-the-art Vector-Quantized Variational Autoencoder + Transformer abnormality detection model. We showcase that this spatial conditioning mechanism statistically-significantly improves model performance on whole-body data compared to the same model without conditioning, while allowing the model to perform inference at varying data geometries.

关键词： image resolution

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Segmentation Distortion: Quantifying Segmentation Uncertainty Under Domain Shift via the Effects of Anomalous Activations 1

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

作者： Lennartz, Jonathan Schultz, thomas Univ Bonn Bonn Germany Lamarr Inst Machine Learning & Artificial Intelli Bonn Germany

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Domain shift occurs when training U-Nets for medical image segmentation with images from one device, but applying them to images from a different device. this often reduces accuracy, and it poses a challenge for uncertainty quantification, when incorrect segmentations are produced with high confidence. Recent work proposed to detect such failure cases via anomalies in feature space: Activation patterns that deviate from those observed during training are taken as an indication that the input is not handled well by the network, and its output should not be trusted. However, such latent space distances primarily detect whether images are from different scanners, not whether they are correctly segmented. therefore, we propose a novel segmentation distortion measure for uncertainty quantification. It uses an autoencoder to make activations more similar to those that were observed during training, and propagates the result through the remainder of the U-Net. We demonstrate that the extent to which this affects the segmentation correlates much more strongly with segmentation errors than distances in activation space, and that it quantifies uncertainty under domain shift better than entropy in the output of a single U-Net, or an ensemble of U-Nets.

关键词： Uncertainty Quantification image Segmentation Anomaly Propagation

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Fed-CoT: Co-teachers for Federated Semi-supervised MS Lesion Segmentation 26th

Fed-CoT: Co-teachers for Federated Semi-supervised MS Lesion...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI) / 8th ISIC Workshop / 1st Care-AI Workshop / 1st MedAGI Workshop / 4th DeCaF Workshop

作者： Zhan, Geng Deng, Jiajun Cabezas, Mariano Ouyang, Wanli Barnett, Michael Wang, Chenyu Sydney Neuroimaging Anal Ctr Sydney Australia Univ Sydney Sydney Australia Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9783031474002;9783031474019

Federated learning (FL) is an emerging technique for obtaining a global model while ensuring the data privacy of each client, which is particularly significant in protecting the patients' privacy when conducting medical image analysis. However, previous FL methods for medical images typically assume a fully supervised setting where each client's data is fully annotated, disregarding the fact that obtaining such extensive annotations may present significant obstacles due to the need for specialized expertise and the associated overhead costs. In this work, we focus on lesion segmentation for brain MRI images and propose a federated semi-supervised framework to address this problem. Formally, we introduce a Federated Co-Teachers algorithm (Fed-CoT) that extends the prevalent Mean Teacher algorithm into the federated learning framework, and demonstrate its effectiveness. Particularly, in Fed-CoT, two teacher models, namely sync-teacher and async-teacher, which capitalize on different weight updating schemes are leveraged to provide informative consistency regularization and to avoid overfitting to the noise of targets generated by a single teacher model. Our experimental results validate the merits of our proposed method and suggest that the federated learning model can benefit from extra data even without annotations. this approach relaxes the requirement for client participation in federated learning, making it easier to deploy in real applications.

关键词： Federated learning medical image Segmentation Semi-supervised learning

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DWU-Net: A Dual-Branch U-Shaped Multi-Modal medical image Segmentation Network Based on Wavelet Transform 1st

DWU-Net: A Dual-Branch U-Shaped Multi-Modal Medical Image Se...

引用

1st MICCAI Challenge Comprehensive Analysis and computing of Real-World medical images, CARE 2024 Held in Conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Li, Jihao Han, Xian-Hua Qiao, Xu Sun, Jiande Wang, Jian School of Information Science and Engineering Shandong Normal University Jinan China Graduate School of Artificial Intelligence and Science Rikkyo University Tokyo Japan School of Control Science and Engineering Shandong University Jinan China

ISBN: (纸本)9783031870088

Cardiovascular disease is one of the most difficult diseases to treat. Segmentation of cardiac images is an essential approach to improve the effectiveness and accuracy of cardiovascular disease treatment and intervention. Cardiac image segmentation, however, remains a challenging task, especially for effectively and accurately segmenting the multimodal images and simultaneously segmenting multiple heart substructures. In this paper, we propose a dual-branch U-shaped network based on wavelet transform, called DWU-Net. First, we convert the multimodal medical images into low-frequency and high-frequency images using wavelet transform to narrow the multimodal feature differences. the network consists of two branches of U-shaped sub-network, which accepting low-frequency and high-frequency images, respectively. Low-frequency and high-frequency features extracted by the corresponding branch are fused in deep levels and the final segmentation is achieved by both supervising on segmentation results from fused features and self-supervising among segmentation results from the two branches. the proposed model was verified on the Whole Heart Segmentation ++ (WHS++) dataset, showing greatly improved segmentation results compared with state-of-the-art methods. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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Chest X-ray image Classification: A Causal Perspective 1

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

作者： Nie, Weizhi Zhang, Chen Song, Dan Bai, Yunpeng Xie, Keliang Liu, An-An Tianjin Univ Tianjin 300072 Peoples R China Tianjin Univ Dept Cardiac Surg Chest Hosp Tianjin 300052 Peoples R China Tianjin Med Univ Clin Sch Thorac Tianjin 300052 Peoples R China Tianjin Med Univ Gen Hosp Dept Crit Care Med Dept Anesthesiol Tianjin 300052 Peoples R China Tianjin Med Univ Gen Hosp Tianjin Inst Anesthesiol Tianjin 300052 Peoples R China

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

the chest X-ray (CXR) is a widely used and easily accessible medical test for diagnosing common chest diseases. Recently, there have been numerous advancements in deep learning-based methods capable of effectively classifying CXR. However, assessing whether these algorithms truly capture the cause-and-effect relationship between diseases and their underlying causes, or merely learn to map labels to images, remains a challenge. In this paper, we propose a causal approach to address the CXR classification problem, which involves constructing a structural causal model (SCM) and utilizing backdoor adjustment to select relevant visual information for CXR classification. Specifically, we design various probability optimization functions to eliminate the influence of confounding factors on the learning of genuine causality. Experimental results demonstrate that our proposed method surpasses the performance of two open-source datasets in terms of classification performance. To access the source code for our approach, please visit: https://***/zc2024/Causal_CXR.

关键词： medical image processing Causal inference Chest X-ray image classification

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FedIIC: Towards Robust Federated Learning for Class-Imbalanced medical image Classification 26th

FedIIC: Towards Robust Federated Learning for Class-Imbalanc...

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

作者： Wu, Nannan Yu, Li Yang, Xin Cheng, Kwang-Ting Yan, Zengqiang Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan Peoples R China Hong Kong Univ Sci & Technol Sch Engn Hong Kong Peoples R China

ISBN: (纸本)9783031438943;9783031438950

Federated learning (FL), training deep models from decentralized data without privacy leakage, has shown great potential in medical image computing recently. However, considering the ubiquitous class imbalance in medical data, FL can exhibit performance degradation, especially for minority classes (e.g. rare diseases). Existing methods towards this problem mainly focus on training a balanced classifier to eliminate class prior bias among classes, but neglect to explore better representation to facilitate classification performance. In this paper, we present a privacy-preserving FL method named FedIIC to combat class imbalance from two perspectives: feature learning and classifier learning. In feature learning, two levels of contrastive learning are designed to extract better class-specific features with imbalanced data in FL. In classifier learning, per-class margins are dynamically set according to real-time difficulty and class priors, which helps the model learn classes equally. Experimental results on publicly-available datasets demonstrate the superior performance of FedIIC in dealing with both real-world and simulated multi-source medical imaging data under class imbalance. Code is available at https://***/wnn2000/FedIIC.

关键词： Federated learning Class imbalance Contrastive learning Classification

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Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction 26th

Dense Transformer based Enhanced Coding Network for Unsuperv...

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

作者： Xie, Wangduo Blaschko, Matthew B. Katholieke Univ Leuven Dept ESAT Ctr Proc Speech & Images Leuven Belgium

ISBN: (纸本)9783031439063;9783031439070

CT images corrupted by metal artifacts have serious negative effects on clinical diagnosis. Considering the difficulty of collecting paired data with ground truth in clinical settings, unsupervised methods for metal artifact reduction are of high interest. However, it is difficult for previous unsupervised methods to retain structural information from CT images while handling the non-local characteristics of metal artifacts. To address these challenges, we proposed a novel Dense Transformer based Enhanced Coding Network (DTEC-Net) for unsupervised metal artifact reduction. Specifically, we introduce a Hierarchical Disentangling Encoder, supported by the high-order dense process, and transformer to obtain densely encoded sequences with long-range correspondence. then, we present a second-order disentanglement method to improve the dense sequence's decoding process. Extensive experiments and model discussions illustrate DTEC-Net's effectiveness, which outperforms the previous state-of-the-art methods on a benchmark dataset, and greatly reduces metal artifacts while restoring richer texture details.

关键词： Metal artifact reduction CT image restoration Unsupervised learning Enhanced coding

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