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

共 2882 条记录，以下是251-260 订阅

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Additional Positive Enables Better Representation Learning for medical images 26th

Additional Positive Enables Better Representation Learning f...

引用

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

作者： Zeng, Dewen Wu, Yawen Hu, Xinrong Xu, Xiaowei Hu, Jingtong Shi, Yiyu Univ Notre Dame Notre Dame IN 46556 USA Univ Pittsburgh Pittsburgh PA USA Guangdong Prov Peoples Hosp Guangzhou Peoples R China

ISBN: (纸本)9783031439063;9783031439070

this paper presents a new way to identify additional positive pairs for BYOL, a state-of-the-art (SOTA) self-supervised learning framework, to improve its representation learning ability. Unlike conventional BYOL which relies on only one positive pair generated by two augmented views of the same image, we argue that information from different images with the same label can bring more diversity and variations to the target features, thus benefiting representation learning. To identify such pairs without any label, we investigate TracIn, an instance-based and computationally efficient influence function, for BYOL training. Specifically, TracIn is a gradient-based method that reveals the impact of a training sample on a test sample in supervised learning. We extend it to the self-supervised learning setting and propose an efficient batchwise per-sample gradient computation method to estimate the pairwise TracIn for representing the similarity of samples in the mini-batch during training. For each image, we select the most similar sample from other images as the additional positive and pull their features together with BYOL loss. Experimental results on two public medical datasets (i.e., ISIC 2019 and ChestX-ray) demonstrate that the proposed method can improve the classification performance compared to other competitive baselines in both semi-supervised and transfer learning settings.

关键词： self-supervised learning representation learning medical image classification

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SAMConvex: Fast Discrete Optimization for CT Registration Using Self-supervised Anatomical Embedding and Correlation Pyramid 26th

SAMConvex: Fast Discrete Optimization for CT Registration Us...

引用

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

作者： Li, Zi Tian, Lin Mok, Tony C. W. Bai, Xiaoyu Wang, Puyang Ge, Jia Zhou, Jingren Lu, Le Ye, Xianghua Yan, Ke Jin, Dakai DAMO Acad Alibaba Grp Hangzhou Peoples R China Univ N Carolina Chapel Hill NC 27515 USA Zhejiang Univ Coll Med Affiliated Hosp 1 Hangzhou Peoples R China Hupan Lab Hangzhou 310023 Peoples R China

ISBN: (纸本)9783031439988;9783031439995

Estimating displacement vector field via a cost volume computed in the feature space has shown great success in image registration, but it suffers excessive computation burdens. Moreover, existing feature descriptors only extract local features incapable of representing the global semantic information, which is especially important for solving large transformations. To address the discussed issues, we propose SAMConvex, a fast coarse-to-fine discrete optimization method for CT registration that includes a decoupled convex optimization procedure to obtain deformation fields based on a self-supervised anatomical embedding (SAM) feature extractor that captures both local and global information. To be specific, SAMConvex extracts per-voxel features and builds 6D correlation volumes based on SAM features, and iteratively updates a flow field by performing lookups on the correlation volumes with a coarse-to-fine scheme. SAMConvex outperforms the state-of-the-art learning-based methods and optimization-based methods over two inter-patient registration datasets (Abdomen CT and HeadNeck CT) and one intra-patient registration dataset (Lung CT). Moreover, as an optimization-based method, SAMConvex only takes similar to 2 s (similar to 5 s with instance optimization) for one paired images.

关键词： medical image Registration Large Deformation

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Multi-shot Prototype Contrastive Learning and Semantic Reasoning for medical image Segmentation 26th

Multi-shot Prototype Contrastive Learning and Semantic Reaso...

引用

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

作者： Song, Yuhui Du, Xiuquan Zhang, Yanping Xu, Chenchu Anhui Univ Artificial Intelligence Inst Hefei Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (纸本)9783031439001;9783031439018

Despite the remarkable achievements made by deep convolutional neural networks in medical image segmentation, the limitation that they rely heavily on high-precision and intensively annotated samples makes it difficult to adapt to novel classes that have not been seen before. Few-shot learning is introduced to solve these challenges by learning the generalized representation of a semantic class from very few annotated support samples that can be used as a reference for unannotated query samples. In this paper, instead of averaging multiple support prototypes, we propose a multi-shot prototype contrastive learning and semantic reasoning network (MPSNet) for medical image segmentation. the multi-shot learning network exists independently within the support set, obtains effective semantic features for support images and gives priority to training the core segmentation model of prototype contrastive learning. We also propose a semantic reasoning network that takes the prior semantic features and prior segmentation model learned from the support set as the immediate and necessary conditions for the query image to deduce its segmentation mask. the proposed method is verified to be superior to the state-of-the-art methods on three public datasets, revealing its powerful segmentation and generalization abilities. Code: https://***/H51705/FSS_MPSNet.

关键词： medical image segmentation Multi-shot learning Prototype contrastive learning Semantic reasoning

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Enhancing Cloud computing Data Security in Attribute-based Encryption Schemes using Cryptographic Algorithm 5

Enhancing Cloud Computing Data Security in Attribute-based E...

引用

5th international conference on image Processing and Capsule Networks, ICIPCN 2024

作者： Yuvarani, R. Mahaveerakannan, R. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamilnadu Chennai602105 India

ISBN: (纸本)9798350367171

Attribute-Based Encryption (ABE) is a powerful cryptographic technology used in cloud computing and data security. It allows data owners to control access to encrypted data based on specific characteristics rather than individual user IDs. this makes sure that only systems that satisfy certain requirements or people, who are allowed may decode and view the data. Because of its dynamic and adaptable approach to access management, ABE is a great fit for situations with complex and dynamic access requirements. In response to the threat posed by Quantum computing, this research study focused on developing Post-Quantum ABE schemes that maintain security while also addressing attributes like verifiability, user privacy, and revocability. A novel ABE system founded on rank metric codes has been proposed. the proposed approach is built on low rank parity check codes with a robust resistance against diverse forms of attacks with a compact key size and rapid execution time, demonstrating notable efficiency when compared to other cryptographic systems. this represents a substantial stride forward in enhancing Cloud computing in the era of Quantum computing. © 2024 IEEE.

关键词： Cloud security

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A General Computationally-Efficient 3D Reconstruction Pipeline for Multiple images with Point Clouds 26th

A General Computationally-Efficient 3D Reconstruction Pipeli...

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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： Wu, Qingyang Shen, Yiqing Ke, Jing Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China Johns Hopkins Univ Dept Comp Sci Baltimore MD USA Univ New South Wales Sch Comp Sci & Engn Sydney Australia

ISBN: (纸本)9783031474002;9783031474019

Histology images are the golden standard for medical diagnostic analysis. However, 2D images can lose some critical information, such as the spatial structure of blood vessels. therefore, it is necessary to perform 3D reconstruction for the histology images. At the same time, due to the differences between institutions and hospitals, a general 3D reconstruction method is needed. In this work, we propose a 3D reconstruction pipeline that is compatible with Whole Slide Imaging (WSI) and can also be applied to other imaging modalities such as CT images, MRI images, and immunohistochemistry images. through semantic segmentation, point cloud construction and registration, and 3D rendering, we can reconstruct serialized images into 3D models. By optimizing the pipeline workflow, we can significantly reduce the computation workload required for the 3D reconstruction of high-resolution images and thus save time. In clinical practice, our method helps pathologists triage and evaluate tumor tissues with real-time 3D visualization.

关键词： Histology image Point Clouds 3D Reconstruction

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EGE-UNet: An Efficient Group Enhanced UNet for Skin Lesion Segmentation 26th

EGE-UNet: An Efficient Group Enhanced UNet for Skin Lesion S...

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

作者： Ruan, Jiacheng Xie, Mingye Gao, Jingsheng Liu, Ting Fu, Yuzhuo Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9783031439001;9783031439018

Transformer and its variants have been widely used for medical image segmentation. However, the large number of parameter and computational load of these models make them unsuitable for mobile health applications. To address this issue, we propose a more efficient approach, the Efficient Group Enhanced UNet (EGE-UNet). We incorporate a Group multi-axis Hadamard Product Attention module (GHPA) and a Group Aggregation Bridge module (GAB) in a lightweight manner. the GHPA groups input features and performs Hadamard Product Attention mechanism (HPA) on different axes to extract pathological information from diverse perspectives. the GAB effectively fuses multi-scale information by grouping low-level features, high-level features, and a mask generated by the decoder at each stage. Comprehensive experiments on the ISIC2017 and ISIC2018 datasets demonstrate that EGEUNet outperforms existing state-of-the-art methods. In short, compared to the TransFuse, our model achieves superior segmentation performance while reducing parameter and computation costs by 494x and 160x, respectively. Moreover, to our best knowledge, this is the first model with a parameter count limited to just 50KB. Our code is available at https://***/JCruan519/EGE-UNet.

关键词： medical image segmentation Light-weight model mobile health

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Devil is in Channels: Contrastive Single Domain Generalization for medical image Segmentation 26th

Devil is in Channels: Contrastive Single Domain Generalizati...

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

作者： Hu, Shishuai Liao, Zehui Xia, Yong Northwestern Polytech Univ Natl Engn Lab Integrated Aerospace Ground Ocean B Xian 710072 Peoples R China

ISBN: (纸本)9783031439001;9783031439018

Deep learning-based medical image segmentation models suffer from performance degradation when deployed to a new healthcare center. To address this issue, unsupervised domain adaptation and multi-source domain generalization methods have been proposed, which, however, are less favorable for clinical practice due to the cost of acquiring target-domain data and the privacy concerns associated with redistributing the data from multiple source domains. In this paper, we propose a Channel-level Contrastive Single Domain Generalization (C(2)SDG) model for medical image segmentation. In C(2)SDG, the shallower features of each image and its style-augmented counterpart are extracted and used for contrastive training, resulting in the disentangled style representations and structure representations. the segmentation is performed based solely on the structure representations. Our method is novel in the contrastive perspective that enables channel-wise feature disentanglement using a single source domain. We evaluated C(2)SDG against six SDG methods on a multi-domain joint optic cup and optic disc segmentation benchmark. Our results suggest the effectiveness of each module in C(2)SDG and also indicate that C(2)SDG outperforms the baseline and all competing methods with a large margin. the code is available at https://***/ShishuaiHu/CCSDG.

关键词： Single domain generalization medical image segmentation Contrastive learning Feature disentanglement

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MultiTalent: A Multi-dataset Approach to medical image Segmentation 1

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

作者： Ulrich, Constantin Isensee, Fabian Wald, Tassilo Zenk, Maximilian Baumgartner, Michael Maier-Hein, Klaus H. German Canc Res Ctr Div Med Image Comp Heidelberg Germany DKFZ Helmholtz Imaging Heidelberg Germany Heidelberg Univ Hosp Dept Radiat Oncol Pattern Anal & Learning Grp Heidelberg Germany NCT Heidelberg Natl Ctr Tumor Dis NCT Heidelberg Germany Heidelberg Univ Med Fac Heidelberg Heidelberg Germany Heidelberg Univ Fac Math & Comp Sci Heidelberg Germany

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

the medical imaging community generates a wealth of datasets, many of which are openly accessible and annotated for specific diseases and tasks such as multi-organ or lesion segmentation. Current practices continue to limit model training and supervised pre-training to one or a few similar datasets, neglecting the synergistic potential of other available annotated data. We propose MultiTalent, a method that leverages multiple CT datasets with diverse and conflicting class definitions to train a single model for a comprehensive structure segmentation. Our results demonstrate improved segmentation performance compared to previous related approaches, systematically, also compared to single-dataset training using state-of-the-art methods, especially for lesion segmentation and other challenging structures. We show that MultiTalent also represents a powerful foundation model that offers a superior pre-training for various segmentation tasks compared to commonly used supervised or unsupervised pre-training baselines. Our findings offer a new direction for the medical imaging community to effectively utilize the wealth of available data for improved segmentation performance. the code and model weights will be published here: https://***/ MIC- DKFZ/MultiTalent.

关键词： medical image segmentation multitask learning transfer learning foundation model partially labeled datasets

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Scale Federated Learning for Label Set Mismatch in medical image Classification 1

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

作者： Deng, Zhipeng Luo, Luyang Chen, Hao Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Chem & Biol Engn Hong Kong Peoples R China

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Federated learning (FL) has been introduced to the healthcare domain as a decentralized learning paradigm that allows multiple parties to train a model collaboratively without privacy leakage. However, most previous studies have assumed that every client holds an identical label set. In reality, medical specialists tend to annotate only diseases within their area of expertise or interest. this implies that label sets in each client can be different and even disjoint. In this paper, we propose the framework FedLSM to solve the problem of Label Set Mismatch. FedLSM adopts different training strategies on data with different uncertainty levels to efficiently utilize unlabeled or partially labeled data as well as class-wise adaptive aggregation in the classification layer to avoid inaccurate aggregation when clients have missing labels. We evaluated FedLSM on two public real-world medical image datasets, including chest X-ray (CXR) diagnosis with 112,120 CXR images and skin lesion diagnosis with 10,015 dermoscopy images, and showed that it significantly out-performed other state-of-the-art FL algorithms. the code can be found at https://***/dzp2095/FedLSM.

关键词： Federated Learning Label Set Mismatch

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Deblurring Masked Autoencoder Is Better Recipe for Ultrasound image Recognition 26th

Deblurring Masked Autoencoder Is Better Recipe for Ultrasoun...

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

作者： Kang, Qingbo Gao, Jun Li, Kang Lao, Qicheng Sichuan Univ West China Hosp West China Biomed Big Data Ctr Chengdu Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Sichuan Univ Coll Comp Sci Chengdu Peoples R China

ISBN: (纸本)9783031439063;9783031439070

Masked autoencoder (MAE) has attracted unprecedented attention and achieves remarkable performance in many vision tasks. It reconstructs random masked image patches (known as proxy task) during pretraining and learns meaningful semantic representations that can be transferred to downstream tasks. However, MAE has not been thoroughly explored in ultrasound imaging. In this work, we investigate the potential of MAE for ultrasound image recognition. Motivated by the unique property of ultrasound imaging in high noise-to-signal ratio, we propose a novel deblurring MAE approach that incorporates deblurring into the proxy task during pretraining. the addition of deblurring facilitates the pretraining to better recover the subtle details presented in the ultrasound images, thus improving the performance of the downstream classification task. Our experimental results demonstrate the effectiveness of our deblurring MAE, achieving state-of-the-art performance in ultrasound image classification. Overall, our work highlights the potential of MAE for ultrasound image recognition and presents a novel approach that incorporates deblurring to further improve its effectiveness.

关键词： image Deblurring Masked Autoencoders Self-Supervised Learning Ultrasound Recognition

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