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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71-80 订阅

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9th international Workshop on Perinatal, Preterm and Paediatric image Analysis, PIPPI 2024, held in Conjunction with the 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

9th International Workshop on Perinatal, Preterm and Paediat...

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9th international Workshop on Perinatal, Preterm and Paediatric image Analysis, PIPPI 2024, held in Conjunction with the 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

ISBN: (纸本)9783031732591

The proceedings contain 14 papers. The special focus in this conference is on Perinatal, Preterm and Paediatric image Analysis. The topics include: Automatic Disentanglement of Motion in Fetal Low Field MRI Scans;Advanced Framework for Fetal Diffusion MRI: Dynamic Distortion and Motion Correction;Assessing Data Quality on Fetal Brain MRI Reconstruction: A Multi-site and Multi-rater Study;automatic 8-Tissue Segmentation for 6-Month Infant Brains;towards Accurate Fetal Brain Parcellation via Hierarchical Network and Loss;automatic Assessment of Fetal Multi-echo Diffusion Weighted Scans;rethinking Fetal Brain Atlas Construction: A Deep Learning Perspective;Enhancing Prenatal Diagnosis: Automated Fetal Brain MRI Morphometry;Towards Automated Multi-regional Lung Parcellation for 0.55-3T 3D T2w Fetal MRI;Fetal Body Parts Segmentation Using Volumetric MRI Reconstructions;generative Diffusion Model Bootstraps Zero-Shot Classification of Fetal Ultrasound images in Underrepresented African Populations;semi-supervised Three-Dimensional Detection of Congenital Brain Anomalies in First Trimester Ultrasound.

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13th international Workshop on Clinical image-based Procedures: Towards Holistic Patient Models for Personalized Healthcare, CLIP 2024 held in conjunction with the international conference on medical image computing and computer assisted intervention, MICCAI 2024

13th International Workshop on Clinical Image-based Procedur...

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13th international Workshop on Clinical image-based Procedures: Towards Holistic Patient Models for Personalized Healthcare, CLIP 2024 held in conjunction with the international conference on medical image computing and computer assisted intervention, MICCAI 2024

ISBN: (纸本)9783031730825

The proceedings contain 9 papers. The special focus in this conference is on Clinical image-based Procedures: Towards Holistic Patient Models for Personalized Healthcare. The topics include: Automated Multi-View Planning for Endovascular Aneurysm Repair Procedures;HTSeg: Hybrid Two-Stage Segmentation Framework for Intestine Segmentation from CT Volumes;enPrO: Enhancing Precision Through Optimization in image-Guided Spine Surgical Procedures;patient-Specific 3D Burn Size Estimation;Novel CBCT-MRI Registration Approach for Enhanced Analysis of Temporomandibular Degenerative Joint Disease;abdominal Ultrasound Similarity Analysis for Quantitative Longitudinal Liver Fibrosis Staging;real-Time Device Detection with Rotated Bounding Boxes and Its Clinical Application.

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Diffusion Transformer U-Net for medical image Segmentation 26th

Diffusion Transformer U-Net for Medical Image Segmentation

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

作者： Chowdary, G. Jignesh Yin, Zhaozheng SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (纸本)9783031439001;9783031439018

Diffusion model has shown its power on various generation tasks. When applying the diffusion model in medical image segmentation, there are a few roadblocks to remove: the semantic features required for the conditioning of the diffusion process are not well aligned with the noise embedding;and the U-Net backbone employed in these diffusion models is not sensitive to contextual information that is essential during the reverse diffusion process for accurate pixel-level segmentation. To overcome these limitations, we present a cross-attention module to enhance the conditioning from source images, and a transformer based U-Net with multi-sized windows for the extraction of various scales of contextual information. Evaluated on five benchmark datasets with different imaging modalities including Kvasir-Seg, CVC Clinic DB, ISIC 2017, ISIC 2018, and Refuge, our diffusion transformer U-Net achieves great generalization ability and outperforms all the state-of-the-art models on these datasets.

关键词： Diffusion model Transformer U-Net medical image Segmentation

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Operating Critical Machine Learning Models in Resource Constrained Regimes 26th

Operating Critical Machine Learning Models in Resource Const...

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

作者： Selvan, Raghavendra Schon, Julian Dam, Erik B. Univ Copenhagen Dept Comp Sci Copenhagen Denmark Univ Copenhagen Dept Neurosci Copenhagen Denmark

ISBN: (纸本)9783031474248;9783031474255

The accelerated development of machine learning methods, primarily deep learning, are causal to the recent breakthroughs in medical image analysis and computer aided intervention. The resource consumption of deep learning models in terms of amount of training data, compute and energy costs are known to be massive. These large resource costs can be barriers in deploying these models in clinics, globally. To address this, there are cogent efforts within the machine learning community to introduce notions of resource efficiency. For instance, using quantisation to alleviate memory consumption. While most of these methods are shown to reduce the resource utilisation, they could come at a cost in performance. In this work, we probe into the trade-off between resource consumption and performance, specifically, when dealing with models that are used in critical settings such as in clinics.

关键词： Resource efficiency image Classification Deep Learning

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Regular SE(3) Group Convolutions for Volumetric medical image Analysis 1

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

作者： Kuipers, Thijs P. Bekkers, Erik J. Univ Amsterdam Inst Informat Amsterdam Machine Learning Lab Amsterdam Netherlands

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Regular group convolutional neural networks (G-CNNs) have been shown to increase model performance and improve equivariance to different geometrical symmetries. This work addresses the problem of SE(3), i.e., roto-translation equivariance, on volumetric data. Volumetric image data is prevalent in many medical settings. Motivated by the recent work on separable group convolutions, we devise a SE(3) group convolution kernel separated into a continuous SO(3) (rotation) kernel and a spatial kernel. We approximate equivariance to the continuous setting by sampling uniform SO(3) grids. Our continuous SO(3) kernel is parameterized via RBF interpolation on similarly uniform grids. We demonstrate the advantages of our approach in volumetric medical image analysis. Our SE(3) equivariant models consistently outperform CNNs and regular discrete G-CNNs on challenging medical classification tasks and show significantly improved generalization capabilities. Our approach achieves up to a 16.5% gain in accuracy over regular CNNs.

关键词： geometric deep learning equivariance group convolution SE(3) volumetric data

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Annotator Consensus Prediction for medical image Segmentation with Diffusion Models 26th

Annotator Consensus Prediction for Medical Image Segmentatio...

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

作者： Amit, Tomer Shichrur, Shmuel Shaharabany, Tal Wolf, Lior Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9783031439001;9783031439018

A major challenge in the segmentation of medical images is the large inter- and intra-observer variability in annotations provided by multiple experts. To address this challenge, we propose a novel method for multi-expert prediction using diffusion models. Our method leverages the diffusion-based approach to incorporate information from multiple annotations and fuse it into a unified segmentation map that reflects the consensus of multiple experts. We evaluate the performance of our method on several datasets of medical segmentation annotated by multiple experts and compare it with the state-of-the-art methods. Our results demonstrate the effectiveness and robustness of the proposed method. Our code is publicly available at https://***/tomeramit/Annotator-Consensus-Prediction.

关键词： Multi annotator image segmentation Diffusion Model

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Memory Replay for Continual medical image Segmentation Through Atypical Sample Selection 26th

Memory Replay for Continual Medical Image Segmentation Throu...

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

作者： Bera, Sutanu Ummadi, Vinay Sen, Debashis Mandal, Subhamoy Biswas, Prabir Kumar Indian Inst Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9783031439001;9783031439018

medical image segmentation is critical for accurate diagnosis, treatment planning and disease monitoring. Existing deep learning-based segmentation models can suffer from catastrophic forgetting, especially when faced with varying patient populations and imaging protocols. Continual learning (CL) addresses this challenge by enabling the model to learn continuously from a stream of incoming data without the need to retrain from scratch. In this work, we propose a continual learning-based approach for medical image segmentation using a novel memory replay-based learning scheme. The approach uses a simple and effective algorithm for image selection to create the memory bank by ranking and selecting images based on their contribution to the learning process. We evaluate our proposed algorithm on three different problems and compare it with several baselines, showing significant improvements in performance. Our study highlights the potential of continual learning-based algorithms for medical image segmentation and underscores the importance of efficient sample selection in creating memory banks.

关键词： Memory Replay Continual Learning medical image Segmentation

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PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans 9th

PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans

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

作者： De Santi, Lisa Anita Schlotterer, Jorg Scheschenja, Michael Wessendorf, Joel Nauta, Meike Positano, Vincenzo Seifert, Christin Univ Pisa Pisa Italy Univ Marburg Marburg Germany Univ Mannheim Mannheim Germany Univ Hosp Marburg Dept Diagnost & Intervent Radiol Marburg Germany Datacation Eindhoven Netherlands Fdn Toscana G Monasterio Pisa Italy

ISBN: (纸本)9783031776090;9783031776106

Information from neuroimaging examinations is increasingly used to support diagnoses of dementia, e.g., Alzheimer's disease. While current clinical practice is mainly based on visual inspection and feature engineering, Deep Learning approaches can be used to automate the analysis and to discover new image-biomarkers. Part-prototype neural networks (PP-NN) are an alternative to standard blackbox models, and have shown promising results in general computer vision. PP-NN's base their reasoning on prototypical image regions that are learned fully unsupervised, and combined with a simple-to-understand decision layer. We present PIPNet3D, a PP-NN for volumetric images. We apply PIPNet3D to the clinical diagnosis of Alzheimer's Disease from structural Magnetic Resonance Imaging (sMRI). We assess the quality of prototypes under a systematic evaluation framework, propose new functionally grounded metrics to evaluate brain prototypes and develop an evaluation scheme to assess their coherency with domain experts. Our results show that PIPNet3D is an interpretable, compact model for Alzheimer's diagnosis with its reasoning well aligned to medical domain knowledge. Notably, PIPNet3D achieves the same accuracy as its blackbox counterpart;and removing the remaining clinically irrelevant prototypes from its decision process does not decrease predictive performance.

关键词： Deep Learning XAI Explainable AI Interpretable Deep Learning Part-prototypes MRI Alzheimer

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A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation 9th

A Federated Learning-Friendly Approach for Parameter-Efficie...

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

作者： Asokan, Mothilal Benjamin, Joseph Geo Yaqub, Mohammad Nandakumar, Karthik Mohamed bin Zayed Univ Artificial Intelligence MB Abu Dhabi U Arab Emirates

ISBN: (纸本)9783031776090;9783031776106

Adapting foundation models for medical image analysis requires finetuning them on a considerable amount of data because of extreme distribution shifts between natural (source) data used for pre-training and medical (target) data. However, collecting task-specific medical data for such finetuning at a central location raises many privacy concerns. Although Federated learning (FL) provides an effective means for training on private decentralized data, communication costs in federating large foundation models can quickly become a significant bottleneck, impacting the solution's scalability. In this work, we address this problem of 'efficient communication while ensuring effective learning in FL' by combining the strengths of Parameter-Efficient Fine-tuning (PEFT) with FL. Specifically, we study plug-and-play Low-Rank Adapters (LoRA) in a federated manner to adapt the Segment Anything Model (SAM) for 3D medical image segmentation. Unlike prior works that utilize LoRA and finetune the entire decoder, we critically analyze the contribution of each granular component of SAM on finetuning performance. Thus, we identify specific layers to be federated that are very efficient in terms of communication cost while producing on-par accuracy. Our experiments show that retaining the parameters of the SAM model (including most of the decoder) in their original state during adaptation is beneficial because fine-tuning on small datasets tends to distort the inherent capabilities of the underlying foundation model. On Fed-KiTS, our approach decreases communication cost (similar to 48x.) compared to full fine-tuning while increasing performance (similar to 6%. Dice score) in 3D segmentation tasks. Our approach performs similar to SAMed while achieving similar to 2.8x reduction in communication and parameters to be finetuned. We further validate our approach with experiments on Fed-IXI and Prostate MRI datasets. Our code is available at https://***/BioMedIA-MBZUAI/FLAP-SAM.

关键词： Federated Learning Foundation Model 3D medical image Segmentation Parameter-Efficient Fine-Tuning

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Quantifying the Impact of Population Shift Across Age and Sex for Abdominal Organ Segmentation 2nd

Quantifying the Impact of Population Shift Across Age and...

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2nd international Workshop on Fairness of AI in medical Imaging, FAIMI 2024, and 3rd international Workshop on Ethical and Philosophical Issues in medical Imaging, EPIMI 2024, Held in Conjunction with the international conference on medical image computing and computer assisted interventions, MICCAI 2024

作者： Čevora, Kate Glocker, Ben Bai, Wenjia Department of Computing Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Data Science Institute Imperial College London London United Kingdom

ISBN: (纸本)9783031727863

Deep learning-based medical image segmentation has seen tremendous progress over the last decade, but there is still relatively little transfer into clinical practice. One of the main barriers is the challenge of domain generalisation, which requires segmentation models to maintain high performance across a wide distribution of image data. This challenge is amplified by the many factors that contribute to the diverse appearance of medical images, such as acquisition conditions and patient characteristics. The impact of shifting patient characteristics such as age and sex on segmentation performance remains relatively under-studied, especially for abdominal organs, despite that this is crucial for ensuring the fairness of the segmentation model. We perform the first study to determine the impact of population shift with respect to age and sex on abdominal CT image segmentation, by leveraging two large public datasets, and introduce a novel metric to quantify the impact. We find that population shift is a challenge similar in magnitude to cross-dataset shift for abdominal organ segmentation, and that the effect is asymmetric and dataset-dependent. We conclude that dataset diversity in terms of known patient characteristics is not necessarily equivalent to dataset diversity in terms of image features. This implies that simple population matching to ensure good generalisation and fairness may be insufficient, and we recommend that fairness research should be directed towards better understanding and quantifying medical image dataset diversity in terms of performance-relevant characteristics such as organ morphology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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