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检索条件"任意字段=3rd International Conference on Medical Image Computing and Computer-Assisted Intervention"

共 4110 条记录，以下是91-100 订阅

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CXR-CLIP: Toward Large Scale Chest X-ray Language-image Pre-training 26th

CXR-CLIP: Toward Large Scale Chest X-ray Language-Image Pre-...

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

作者： You, Kihyun Gu, Jawook Ham, Jiyeon Park, Beomhee Kim, Jiho Hong, Eun K. Baek, Woonhyuk Roh, Byungseok Kakaobrain Seongnam South Korea

ISBN: (纸本)9783031438943;9783031438950

A large-scale image-text pair dataset has greatly contributed to the development of vision-language pre-training (VLP) models, which enable zero-shot or few-shot classification without costly annotation. However, in the medical domain, the scarcity of data remains a significant challenge for developing a powerful VLP model. In this paper, we tackle the lack of image-text data in chest X-ray by expanding image-label pair as image-text pair via general prompt and utilizing multiple images and multiple sections in a radiologic report. We also design two contrastive losses, named ICL and TCL, for learning study-level characteristics of medical images and reports, respectively. Our model outperforms the state-of-the-art models trained under the same conditions. Also, enlarged dataset improve the discriminative power of our pre-trained model for classification, while sacrificing marginal retrieval performance. Code is available at https://***/kakaobrain/cxr-clip.

关键词： Chest X-ray Vision-Language Pre-training Contrastive Learning

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Ariadne's Thread: Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images 26th

Ariadne's Thread: Using Text Prompts to Improve Segmentation...

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

作者： Zhong, Yi Xu, Mengqiu Liang, Kongming Chen, Kaixin Wu, Ming Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9783031439001;9783031439018

Segmentation of the infected areas of the lung is essential for quantifying the severity of lung disease like pulmonary infections. Existing medical image segmentation methods are almost uni-modal methods based on image. However, these image-only methods tend to produce inaccurate results unless trained with large amounts of annotated data. To overcome this challenge, we propose a language-driven segmentation method that uses text prompt to improve to the segmentation result. Experiments on the QaTa-COV19 dataset indicate that our method improves the Dice score by 6.09% at least compared to the uni-modal methods. Besides, our extended study reveals the flexibility of multi-modal methods in terms of the information granularity of text and demonstrates that multi-modal methods have a significant advantage over image-only methods in terms of the size of training data required.

关键词： Multi-modal Learning medical image Segmentation

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9th international Workshop on Simulation and Synthesis in medical Imaging, SASHIMI 2024, held in conjunction with the 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

9th International Workshop on Simulation and Synthesis in Me...

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9th international Workshop on Simulation and Synthesis in medical Imaging, SASHIMI 2024, held in conjunction with the 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

ISBN: (纸本)9783031732805

The proceedings contain 19 papers. The special focus in this conference is on Simulation and Synthesis in medical Imaging. The topics include: Adapted nnU-Net: A Robust Baseline for Cross-Modality Synthesis and medical image Inpainting;Beyond MR image Harmonization: Resolution Matters Too;benchmarking Robustness of Endoscopic Depth Estimation with Synthetically Corrupted Data;a Dual-Task Mutual Learning Framework for Predicting Post-thrombectomy Cerebral Hemorrhage;TSynD: Targeted Synthetic Data Generation for Enhanced medical image Classification: Leveraging Epistemic Uncertainty to Improve Model Performance;beyond Intensity Transforms: medical image Synthesis Under Large Deformation;sim2Real in Endoscopy Segmentation with a Novel Structure Aware image Translation;fireflies: Photorealistic Simulation and Optimization of Structured Light Endoscopy;exogenous Agent-Free Synthetic Post-contrast Imaging with a Cascade of Deep Networks for Enhancement Prediction After Tumor Resection. A Parametric-Map Oriented Approach;OCT Scans Simulation Framework for Data Augmentation and Controlled Evaluation of Signal Processing Approaches;enhancing Quantitative image Synthesis Through Pretraining and Resolution Scaling for Bone Mineral Density Estimation from a Plain X-Ray image;latent Pollution Model: The Hidden Carbon Footprint in 3D image Synthesis;Synthesizing Scalable CFD-Enhanced Aortic 4D Flow MRI for Assessing Accuracy and Precision of Deep-Learning image Reconstruction and Segmentation Tasks;MedEdit: Counterfactual Diffusion-Based image Editing on Brain MRI;Using MR Physics for Domain Generalisation and Super-Resolution;Single-Scan mpMRI Calibration of Multi-species Brain Tumor Dynamics with Mass Effect;annotated Biomedical Video Generation Using Denoising Diffusion Probabilistic Models and Flow Fields.

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Hessian-Based Similarity Metric for Multimodal medical image Registration 26th

Hessian-Based Similarity Metric for Multimodal Medical Image...

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

作者： Eskandari, Mohammadreza Gueziri, Houssem-Eddine Collins, D. Louis McGill Univ Dept Biomed Engn Montreal PQ Canada Montreal Neurol Hosp & Inst McConnell Brain Imaging Ctr Montreal PQ Canada McGill Univ Dept Neurol & Neurosurg Montreal PQ Canada

ISBN: (纸本)9783031474248;9783031474255

One of the fundamental elements of both traditional and certain deep learning medical image registration algorithms is measuring the similarity/dissimilarity between two images. In this work, we propose an analytical solution for measuring similarity between two different medical image modalities based on the Hessian of their intensities. First, assuming a functional dependence between the intensities of two perfectly corresponding patches, we investigate how their Hessians relate to each other. Secondly, we suggest a closed-form expression to quantify the deviation from this relationship, given arbitrary pairs of image patches. We propose a geometrical interpretation of the new similarity metric and an efficient implementation for registration. We demonstrate the robustness of the metric to intensity nonuniformities using synthetic bias fields. By integrating the new metric in an affine registration framework, we evaluate its performance for MRI and ultrasound registration in the context of image-guided neurosurgery using target registration error and computation time.

关键词： Multimodal registration Similarity metric image Hessian

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M3D-NCA: Robust 3D Segmentation with Built-In Quality Control 1

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

作者： Kalkhof, John Mukhopadhyay, Anirban Tech Univ Darmstadt Karolinenpl 5 D-64289 Darmstadt Germany

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

medical image segmentation relies heavily on large-scale deep learning models, such as UNet-based architectures. However, the real-world utility of such models is limited by their high computational requirements, which makes them impractical for resource-constrained environments such as primary care facilities and conflict zones. Furthermore, shifts in the imaging domain can render these models ineffective and even compromise patient safety if such errors go undetected. To address these challenges, we propose M3D-NCA, a novel methodology that leverages Neural Cellular Automata (NCA) segmentation for 3D medical images using n-level patchification. Moreover, we exploit the variance in M3D-NCA to develop a novel quality metric which can automatically detect errors in the segmentation process of NCAs. M3D-NCA outperforms the two magnitudes larger UNet models in hippocampus and prostate segmentation by 2% Dice and can be run on a Raspberry Pi 4 Model B (2GB RAM). This highlights the potential of M3D-NCA as an effective and efficient alternative for medical image segmentation in resource-constrained environments.

关键词： Neural Cellular Automata medical image Segmentation Automatic Quality Control

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EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting 9th

EndoGS: Deformable Endoscopic Tissues Reconstruction with Ga...

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27th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Zhu, Lingting Wang, Zhao Cui, Jiahao Jin, Zhenchao Lin, Guying Yu, Lequan Univ Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (纸本)9783031776090;9783031776106

Surgical 3D reconstruction is a critical area of research in robotic surgery, with recent works adopting variants of dynamic radiance fields to achieve success in 3D reconstruction of deformable tissues from single-viewpoint videos. However, these methods often suffer from time-consuming optimization or inferior quality, limiting their adoption in downstream tasks. Inspired by 3D Gaussian Splatting, a recent trending 3D representation, we present EndoGS, applying Gaussian Splatting for deformable endoscopic tissue reconstruction. Specifically, our approach incorporates deformation fields to handle dynamic scenes, depth-guided supervision with spatial-temporal weight masks to optimize 3D targets with tool occlusion from a single viewpoint, and surface-aligned regularization terms to capture the much better geometry. As a result, EndoGS reconstructs and renders high-quality deformable endoscopic tissues from a single-viewpoint video, estimated depth maps, and labeled tool masks. Experiments on DaVinci robotic surgery videos demonstrate that EndoGS achieves superior rendering quality. Code is available at https://***/HKU-MedAI/EndoGS.

关键词： Gaussian Splatting Robotic Surgery 3D Reconstruction

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9th international Skin Imaging Collaboration Workshop, ISIC 2024, 7th international Workshop on Interpretability of Machine Intelligence in medical image computing, iMIMIC 2024, Embodied AI and Robotics for HealTHcare Workshop, EARTH 2024 and 5th MICCAI Workshop on Distributed, Collaborative and Federated Learning, DeCaF 2024 held at 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

9th International Skin Imaging Collaboration Workshop, ISIC ...

引用

9th international Skin Imaging Collaboration Workshop, ISIC 2024, 7th international Workshop on Interpretability of Machine Intelligence in medical image computing, iMIMIC 2024, Embodied AI and Robotics for HealTHcare Workshop, EARTH 2024 and 5th MICCAI Workshop on Distributed, Collaborative and Federated Learning, DeCaF 2024 held at 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

ISBN: (纸本)9783031776090

The proceedings contain 23 papers. The special focus in this conference is on Skin Imaging Collaboration, Interpretability of Machine Intelligence in medical image computing, Embodied AI and Robotics for HealTHcare Workshop and MICCAI Workshop on Distributed, Collaborative and Federated Learning. The topics include: DeCaF 2024 Preface;i2M2Net: Inter/Intra-modal Feature Masking Self-distillation for Incomplete Multimodal Skin Lesion Diagnosis;from Majority to Minority: A Diffusion-Based Augmentation for Underrepresented Groups in Skin Lesion Analysis;segmentation Style Discovery: Application to Skin Lesion images;a Vision Transformer with Adaptive Cross-image and Cross-Resolution Attention;lesion Elevation Prediction from Skin images Improves Diagnosis;DWARF: Disease-Weighted Network for Attention Map Refinement;PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans;Detecting Unforeseen Data Properties with Diffusion Autoencoder Embeddings Using Spine MRI Data;interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis;TextCAVs: Debugging Vision Models Using Text;evaluating Visual Explanations of Attention Maps for Transformer-Based medical Imaging;Exploiting XAI Maps to Improve MS Lesion Segmentation and Detection in MRI;EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting;VISAGE: Video Synthesis Using Action Graphs for Surgery;a Review of 3D Reconstruction Techniques for Deformable Tissues in Robotic Surgery;SurgTrack: CAD-Free 3D Tracking of Real-World Surgical Instruments;MUTUAL: Towards Holistic Sensing and Inference in the Operating Room;Complex-Valued Federated Learning with Differential Privacy and MRI Applications;enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications;federated Impression for Learning with Distributed Heterogeneous Data;A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation;probing the Effic

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MedIM: Boost medical image Representation via Radiology Report-Guided Masking 26th

MedIM: Boost Medical Image Representation via Radiology Repo...

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

作者： Xie, Yutong Gu, Lin Harada, Tatsuya Zhang, Jianpeng Xia, Yong Wu, Qi Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia RIKEN AIP Tokyo Japan Univ Tokyo RCAST Tokyo Japan Northwestern Polytech Univ Sch Comp Sci & Engn Xian Peoples R China

ISBN: (纸本)9783031439063;9783031439070

Masked image modelling (MIM)-based pre-training shows promise in improving image representations with limited annotated data by randomly masking image patches and reconstructing them. However, random masking may not be suitable for medical images due to their unique pathology characteristics. This paper proposes Masked medical image Modelling (MedIM), a novel approach, to our knowledge, the first research that masks and reconstructs discriminative areas guided by radiological reports, encouraging the network to explore the stronger semantic representations from medical images. We introduce two mutual comprehensive masking strategies, knowledge word-driven masking (KWM) and sentence-driven masking (SDM). KWM uses medical Subject Headings (MeSH) words unique to radiology reports to identify discriminative cues mapped to MeSH words and guide the mask generation. SDM considers that reports usually have multiple sentences, each of which describes different findings, and therefore integrates sentencelevel information to identify discriminative regions for mask generation. MedIM integrates both strategies by simultaneously restoring the images masked by KWM and SDM for a more robust and representative medical visual representation. Our extensive experiments on various downstream tasks covering multi-label/class image classification, medical image segmentation, and medical image-text analysis, demonstrate that MedIM with report-guided masking achieves competitive performance. Our method substantially outperforms imageNet pre-training, MIM-based pre-training, and medical image-report pre-training counterparts. Codes are available at https://***/YtongXie/MedIM.

关键词： Semantics

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RadimageGAN – A Multi-modal Dataset-Scale Generative AI for medical Imaging 3rd

RadImageGAN – A Multi-modal Dataset-Scale Generative AI for...

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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： Liu, Zelong Smith, Peyton Lautin, Alexander Zhou, Jieshen Yoo, Maxwell Sullivan, Mikey Li, Haorun Deyer, Louisa Zhou, Alexander Yang, Arnold Yimaz, Alara Zhang, Catherine Grant, James Li, Daiqing Fayad, Zahi A. Huver, Sean Deyer, Timothy Mei, Xueyan BioMedical Engineering and Imaging Institute Icahn School of Medicine at Mount Sinai New YorkNY United States NVIDIA Santa ClaraCA United States East River Medical Imaging New YorkNY United States Department of Radiology Cornell Medicine New YorkNY United States Windreich Department of Artificial Intelligence and Human Health Icahn School of Medicine at Mount Sinai New YorkNY United States

ISBN: (纸本)9783031820069

Deep learning in medical imaging often requires large-scale, high-quality data, or initiation with suitably pre-trained weights. However, medical datasets are limited by data availability, domain-specific knowledge, and privacy concerns, and the creation of large and diverse radiologic databases like RadimageNet is highly resource-intensive. To address these limitations, we introduce RadimageGAN, a multi-modal medical image generator developed by training StyleGAN-XL on the RadimageNet dataset of CT and MRI images and the HyperKvasir dataset of gastrointestinal images. RadimageGAN can generate high-resolution synthetic medical imaging datasets across 12 anatomical regions and 130 pathological classes in 3 modalities. Furthermore, we developed the RadimageGAN-Labeler, which can generate multi-class pixel-wise annotated paired synthetic images and masks for diverse downstream segmentation tasks with minimal manual annotation. Using synthetic auto-labeled data from RadimageGAN can significantly improve performance on four diverse downstream segmentation datasets by augmenting real training data. We find that RadimageGAN can improve model performance and address data scarcity while reducing the resources needed for annotations for segmentation tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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FedContrast-GPA: Heterogeneous Federated Optimization via Local Contrastive Learning and Global Process-Aware Aggregation 26th

FedContrast-GPA: Heterogeneous Federated Optimization via Lo...

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

作者： Zhou, Qin Zheng, Guoyan Shanghai Jiao Tong Univ Inst Med Robot Sch Biomed Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China

ISBN: (纸本)9783031438943;9783031438950

Federated learning is a promising strategy for performing privacy-preserving, distributed learning for medical image segmentation. However, the data-level heterogeneity as well as system-level heterogeneity makes it challenging to optimize. In this paper, we propose to improve Federated optimization via local Contrastive learning and Global Process-aware Aggregation (referred as FedContrast-GPA), aiming to jointly address both data-level and system-level heterogeneity issues. In specific, To address data-level heterogeneity, we propose to learn a unified latent feature space via an intra-client and inter-client local prototype based contrastive learning scheme. Among which, intra-client contrastive learning is adopted to improve the discriminative ability of learned feature embedding at each client, while inter-client contrastive learning is introduced to achieve cross-client distribution perception and alignment in a privacy preserving manner. To address system-level heterogeneity, we further propose a simple yet effective process-aware aggregation scheme to achieve effective straggler mitigation. Experimental results on six prostate segmentation datasets demonstrate large performance boost over existing state-of-the-art methods.

关键词： Heterogeneous federated learning Process-aware aggregation Local prototype learning Contrastive learning

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