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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231-240 订阅

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Contrastive Masked image-Text Modeling for medical Visual Representation Learning 26th

Contrastive Masked Image-Text Modeling for Medical Visual Re...

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

作者： Chen, Cheng Zhong, Aoxiao Wu, Dufan Luo, Jie Li, Quanzheng Massachusetts Gen Hosp Ctr Adv Med Comp & Anal Boston MA 02114 USA Harvard Med Sch Boston MA 02115 USA Harvard Univ Sch Engn & Appl Sci Boston MA 02115 USA Massachusetts Gen Brigham Data Sci Off Boston MA USA

ISBN: (纸本)9783031439032;9783031439049

Self-supervised learning (SSL) of visual representations from paired medical images and text reports has recently shown great promise for various downstream tasks. However, previous work has focused on investigating the effectiveness of two major SSL techniques separately, i.e., contrastive learning and masked autoencoding, without exploring their potential synergies. In this paper, we aim to integrate the strengths of these two techniques by proposing a contrastive masked image-text modeling framework for medical visual representation learning. On one hand, our framework conducts cross-modal contrastive learning between masked medical images and text reports, with a representation decoder being incorporated to recover the misaligned information in the masked images. On the other hand, to further leverage masked autoencoding, a masked image is also required to be able to reconstruct the original image itself and the masked information in the text reports. With pre-training on a large-scale medical image and report dataset, our framework shows complementary benefits of integrating the two SSL techniques on four downstream classification datasets. Extensive evaluations demonstrate consistent improvements of our method over state-of-the-art approaches, especially when very scarce labeled data are available. code is available at https://***/cchen-cc/CMITM.

关键词： image-text representation learning masked autoencoding contrastive learning

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Efficient Registration of Longitudinal Studies for Follow-Up Lesion Assessment by Exploiting Redundancy and Composition of Deformations 26th

Efficient Registration of Longitudinal Studies for Follow-Up...

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

作者： Kuckertz, Sven Heldmann, Stefan Moltz, Jan Hendrik Fraunhofer Inst Digital Med MEVIS Lubeck Germany

ISBN: (纸本)9783031474248;9783031474255

Follow-up assessment of lesions for cancer patients is an important part of radiologists' work. image registration is a key technology to facilitate this task, as it allows for the automatic establishment of correspondences between previous findings and current observations. However, as the number of examinations increases, more registrations must be computed to allow full correspondence assessment between longitudinal studies. We address the challenge of increased computational time and complexity by identifying and eliminating redundant registration procedures and composing deformations from previously performed registrations, thereby significantly reducing the number of registrations required. We evaluate our proposed methods on a dataset consisting of oncological thoracic follow-up CT scans from 260 patients. By grouping series within a study and identifying reference series, we can reduce the total number of registrations required for a patient by an average factor of 27.5 while maintaining comparable registration quality. Additionally composing deformations further reduces the number of registrations by a factor of 1.86, resulting in an overall average reduction factor of 51.4. Since the number of registrations is directly related to the time required to process the input data, the information is availablemore quickly and subsequent examinations can be performed sooner. For a single subject, this results in an exemplary reduction of total computation time from 37.4 to 1.3 min.

关键词： image registration Lesion tracking Follow-up CT

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MDViT: Multi-domain Vision Transformer for Small medical image Segmentation Datasets 26th

MDViT: Multi-domain Vision Transformer for Small Medical Ima...

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

作者： Du, Siyi Bayasi, Nourhan Hamarneh, Ghassan Garbi, Rafeef Univ British Columbia Vancouver BC Canada Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9783031439001;9783031439018

Despite its clinical utility, medical image segmentation (MIS) remains a daunting task due to images' inherent complexity and variability. Vision transformers (ViTs) have recently emerged as a promising solution to improve MIS;however, they require larger training datasets than convolutional neural networks. To overcome this obstacle, data-efficient ViTs were proposed, but they are typically trained using a single source of data, which overlooks the valuable knowledge that could be leveraged from other available datasets. Naivly combining datasets from different domains can result in negative knowledge transfer (NKT), i.e., a decrease in model performance on some domains with non-negligible inter-domain heterogeneity. In this paper, we propose MDViT, the first multi-domain ViT that includes domain adapters to mitigate data-hunger and combat NKT by adaptively exploiting knowledge in multiple small data resources (domains). Further, to enhance representation learning across domains, we integrate a mutual knowledge distillation paradigm that transfers knowledge between a universal network (spanning all the domains) and auxiliary domain-specific network branches. Experiments on 4 skin lesion segmentation datasets show that MDViT outperforms state-of-the-art algorithms, with superior segmentation performance and a fixed model size, at inference time, even as more domains are added. Our code is available at https://***/siyi-wind/MDViT.

关键词： Vision Transformer Data-efficiency Multi-domain Learning medical image Segmentation Dermatology

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Towards AI-Driven Radiology Education: A Self-supervised Segmentation-Based Framework for High-Precision medical image Editing 26th

Towards AI-Driven Radiology Education: A Self-supervised Seg...

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

作者： Kobayashi, Kazuma Gu, Lin Hataya, Ryuichiro Miyake, Mototaka Takamizawa, Yasuyuki Ito, Sono Watanabe, Hirokazu Yoshida, Yukihiro Yoshimura, Hiroki Harada, Tatsuya Hamamoto, Ryuji Natl Canc Ctr Tokyo Japan RIKEN Ctr Adv Intelligence Project Tokyo Japan Univ Tokyo Tokyo Japan RIKEN Informat R&D & Strategy Headquarters Tokyo Japan Natl Canc Ctr Tokyo Japan Hiroshima Univ Sch Med Hiroshima Japan

ISBN: (纸本)9783031438943;9783031438950

medical education is essential for providing the best patient care in medicine, but creating educational materials using real-world data poses many challenges. For example, the diagnosis and treatment of a disease can be affected by small but significant differences in medical images;however, collecting images to highlight such differences is often costly. therefore, medical image editing, which allows users to create their intended disease characteristics, can be useful for education. However, existing image-editing methods typically require manually annotated labels, which are labor-intensive and often challenging to represent fine-grained anatomical elements precisely. Herein, we present a novel algorithm for editing anatomical elements using segmentation labels acquired through self-supervised learning. Our self-supervised segmentation achieves pixel-wise clustering under the constraint of invariance to photometric and geometric transformations, which are assumed not to change the clinical interpretation of anatomical elements. the user then edits the segmentation map to produce a medical image with the intended detailed findings. Evaluation by five expert physicians demonstrated that the edited images appeared natural as medical images and that the disease characteristics were accurately reproduced.

关键词： image editing Self-supervised segmentation Education

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M-GenSeg: Domain Adaptation for Target Modality Tumor Segmentation with Annotation-Efficient Supervision 26th

M-GenSeg: Domain Adaptation for Target Modality Tumor Segmen...

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

作者： Alefsen, Malo Vorontsov, Eugene Kadoury, Samuel Ecole Polytech Montreal Montreal PQ Canada CHUM Ctr Rech Montreal PQ Canada Paige Montreal PQ Canada

ISBN: (纸本)9783031439001;9783031439018

Automated medical image segmentation using deep neural networks typically requires substantial supervised training. However, these models fail to generalize well across different imaging modalities. this shortcoming, amplified by the limited availability of expert annotated data, has been hampering the deployment of such methods at a larger scale across modalities. To address these issues, we propose M-GenSeg, a new semi-supervised generative training strategy for cross-modality tumor segmentation on unpaired bi-modal datasets. With the addition of known healthy images, an unsupervised objective encourages the model to disentangling tumors from the background, which parallels the segmentation task. then, by teaching the model to convert images across modalities, we leverage available pixel-level annotations from the source modality to enable segmentation in the unannotated target modality. We evaluated the performance on a brain tumor segmentation dataset composed of four different contrast sequences from the public BraTS 2020 challenge data. We report consistent improvement in Dice scores over state-of-the-art domain-adaptive baselines on the unannotated target modality. Unlike the prior art, M-GenSeg also introduces the ability to train with a partially annotated source modality.

关键词： image Segmentation Semi-supervised Learning Unpaired image-to-image Translation

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Decoupled Consistency for Semi-supervised medical image Segmentation 26th

Decoupled Consistency for Semi-supervised Medical Image Segm...

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

作者： Chen, Faquan Fei, Jingjing Chen, Yaqi Huang, Chenxi Xiamen Univ Schoor Informat Xiamen Peoples R China SenseTime Res Shanghai Peoples R China

ISBN: (纸本)9783031439063;9783031439070

By fully utilizing unlabeled data, the semi-supervised learning (SSL) technique has recently produced promising results in the segmentation of medical images. Pseudo labeling and consistency regularization are two effective strategies for using unlabeled data. Yet, the traditional pseudo labeling method will filter out low-confidence pixels. the advantages of both high- and low-confidence data are not fully exploited by consistency regularization. therefore, neither of these two methods can make full use of unlabeled data. We proposed a novel decoupled consistency semi-supervised medical image segmentation framework. First, the dynamic threshold is utilized to decouple the prediction data into consistent and inconsistent parts. For the consistent part, we use the method of cross pseudo supervision to optimize it. For the inconsistent part, we further decouple it into unreliable data that is likely to occur close to the decision boundary and guidance data that is more likely to emerge near the high-density area. Unreliable data will be optimized in the direction of guidance data. We refer to this action as directional consistency. Furthermore, in order to fully utilize the data, we incorporate feature maps into the training process and calculate the loss of feature consistency. A significant number of experiments have demonstrated the superiority of our proposed method. the code is available at https:// ***/wxfaaaaa/DCNet.

关键词： Semi-supervised Learning image Segementation Consistency Regularization

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Learning Expected Appearances for Intraoperative Registration During Neurosurgery 26th

Learning Expected Appearances for Intraoperative Registratio...

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

作者： Haouchine, Nazim Dorent, Reuben Juvekar, Parikshit Torio, Erickson Wells, William M., III Kapur, Tina Golby, Alexandra J. Frisken, Sarah Harvard Med Sch Brigham & Womens Hosp Boston MA 02115 USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9783031439957;9783031439964

We present a novel method for intraoperative patient-to-image registration by learning Expected Appearances. Our method uses preoperative imaging to synthesize patient-specific expected views through a surgical microscope for a predicted range of transformations. Our method estimates the camera pose by minimizing the dissimilarity between the intraoperative 2D view through the optical microscope and the synthesized expected texture. In contrast to conventional methods, our approach transfers the processing tasks to the preoperative stage, reducing thereby the impact of low-resolution, distorted, and noisy intraoperative images, that often degrade the registration accuracy. We applied our method in the context of neuronavigation during brain surgery. We evaluated our approach on synthetic data and on retrospective data from 6 clinical cases. Our method outperformed state-of-the-art methods and achieved accuracies that met current clinical standards.

关键词： Intraoperative Registration image-guided Neurosurgery Augmented Reality Neural image Analogy 3D Pose Estimation

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Non-iterative Coarse-to-Fine Transformer Networks for Joint Affine and Deformable image Registration 26th

Non-iterative Coarse-to-Fine Transformer Networks for Joint ...

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

作者： Meng, Mingyuan Bi, Lei Fulham, Michael Feng, Dagan Kim, Jinman Univ Sydney Sch Comp Sci Sydney NSW Australia Shanghai Jiao Tong Univ Inst Translat Med Shanghai Peoples R China Royal Prince Alfred Hosp Dept Mol Imaging Sydney NSW Australia Shanghai Jiao Tong Univ Med X Res Inst Shanghai Peoples R China

ISBN: (纸本)9783031439988;9783031439995

image registration is a fundamental requirement for medical image analysis. Deep registration methods based on deep learning have been widely recognized for their capabilities to perform fast end-to-end registration. Many deep registration methods achieved state-of-the-art performance by performing coarse-to-fine registration, where multiple registration steps were iterated with cascaded networks. Recently, Non-Iterative Coarse-to-finE (NICE) registration methods have been proposed to perform coarse-to-fine registration in a single network and showed advantages in both registration accuracy and runtime. However, existing NICE registrationmethods mainly focus on deformable registration, while affine registration, a common prerequisite, is still reliant on time-consuming traditional optimization-based methods or extra affine registration networks. In addition, existing NICE registration methods are limited by the intrinsic locality of convolution operations. Transformers may address this limitation for their capabilities to capture long-range dependency, but the benefits of using transformers for NICE registration have not been explored. In this study, we propose a Non-Iterative Coarse-to-finE Transformer network (NICE-Trans) for image registration. Our NICE-Trans is the first deep registrationmethod that (i) performs joint affine and deformable coarse-to-fine registration within a single network, and (ii) embeds transformers into a NICE registration framework to model long-range relevance between images. Extensive experiments with seven public datasets show that our NICE-Trans outperforms state-of-the-art registration methods on both registration accuracy and runtime.

关键词： image Registration Coarse-to-fine Registration Transformer

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Investigating Data Memorization in 3D Latent Diffusion Models for medical image Synthesis 3rd

Investigating Data Memorization in 3D Latent Diffusion Model...

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3rd Workshop on Deep Generative Models for medical image computing and computer assisted intervention (DGM4MICCAI) at the 26th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Dar, Salman Ul Hassan Ghanaat, Arman Kahmann, Jannik Ayx, Isabelle Papavassiliu, theano Schoenberg, Stefan O. Engelhardt, Sandy Heidelberg Univ Hosp Dept Internal Med 3 Grp Artificial Intelligence CardiovascularMed D-69120 Heidelberg Germany AI Hlth Innovat Cluster Heidelberg Germany German Ctr Cardiovasc Res DZHK Partner Site Heidelberg Mannheim Heidelberg Germany Heidelberg Univ Univ Med Ctr Mannheim Dept Radiol & Nucl Med Theodor Kutzer Ufer 1-3 D-68167 Mannheim Germany Univ Med Ctr Mannheim Dept Med Cardiol 1 Theodor Kutzer Ufer 1-3 D-68167 Mannheim Germany

ISBN: (纸本)9783031537660;9783031537677

Generative latent diffusion models have been established as state-of-the-art in data generation. One promising application is generation of realistic synthetic medical imaging data for open data sharing without compromising patient privacy. Despite the promise, the capacity of such models to memorize sensitive patient training data and synthesize samples showing high resemblance to training data samples is relatively unexplored. Here, we assess the memorization capacity of 3D latent diffusion models on photon-counting coronary computed tomography angiography and knee magnetic resonance imaging datasets. To detect potential memorization of training samples, we utilize self-supervised models based on contrastive learning. Our results suggest that such latent diffusion models indeed memorize training data, and there is a dire need for devising strategies to mitigate memorization.

关键词： Deep generative models Latent diffusion Data memorization Patient privacy Contrastive learning

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Retinal Scan Denoising Using Generative Adversarial Networks: A Deep Convolutional Approach 5

Retinal Scan Denoising Using Generative Adversarial Networks...

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5th international conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Gowthami, G. Mano Shankari, J. Babu, Tina St. Francis de Sales College Department of Computer Applications Bengaluru India Alliance School of Advanced Computing Department of Computer Science and Engineering Alliance University Bengaluru India

ISBN: (纸本)9798331518097

image denoising is needed if there is a need to remove noise while preserving details or structures in images in computing vision and image processing. Recent advancements in generative adversarial networks (GANs) and convolutional neural networks (CNNs) have only improved the application of denoising techniques in varying ways. these methods give clearer and more accurate images since these approaches are able to separate noise from image substance. One of the main use of application of the image denoising is that they enhance the images in media, movies and also in medical field where high quality of images are required for diagnosing the diseases. this paper examines the significance of image denoising and introduces a novel GAN-based retinal scan denoising technique that outperforms both CNN-based denoising and conventional Gaussian filtering, achieving higher performance (PSNR: 27). It outperforms other approaches with SCD of 10 dB, SSIM of 0.866 and MSE of 18.99. In this paper, we propose a GAN-based solution for denoising images and leveraging deep learning-based techniques for evaluating the quality of retinal scans and we demonstrate the potential of our approach for enhancing the quality of the images. © 2024 IEEE.

关键词： Generative adversarial networks

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