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检索条件"任意字段=8th International Conference on Medical Image Computing and Computer-Assisted Intervention"

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Input Augmentation with SAM: Boosting medical image Segmentation with Segmentation Foundation Model 26th

Input Augmentation with SAM: Boosting Medical Image Segmenta...

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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： Zhang, Yizhe Zhou, Tao Wang, Shuo Liang, Peixian Zhang, Yejia Chen, Danny Z. Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Fudan Univ Sch Basic Med Sci Digital Med Res Ctr Shanghai 200032 Peoples R China Shanghai Key Lab MICCAI Shanghai 200032 Peoples R China Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

ISBN: (纸本)9783031474002;9783031474019

the Segment Anything Model (SAM) is a recently developed large model for general-purpose segmentation for computer vision tasks. SAM was trained using 11 million images with over 1 billion masks and can produce segmentation results for a wide range of objects in natural scene images. SAM can be viewed as a general perception model for segmentation (partitioning images into semantically meaningful regions). thus, how to utilize such a large foundation model for medical image segmentation is an emerging research target. this paper shows that although SAM does not immediately give high-quality segmentation for medical image data, its generated masks, features, and stability scores are useful for building and training better medical image segmentation models. In particular, we demonstrate how to use SAM to augment image input for commonly-used medical image segmentation models (e.g., U-Net). Experiments on three segmentation tasks show the effectiveness of our proposed SAMAug method.

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Empirical Analysis of a Segmentation Foundation Model in Prostate Imaging 26th

Empirical Analysis of a Segmentation Foundation Model in Pro...

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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： Kim, Heejong Butoi, Victor Ion Dalca, Adrian V. Sabuncu, Mert R. Weill Cornell Med Dept Radiol New York NY 10065 USA Cornell Univ Sch Elect & Comp Engn Ithaca NY USA Cornell Tech Ithaca NY USA Massachusetts Gen Hosp Martinos Ctr Biomed Imaging Boston MA USA Harvard Med Sch Boston MA USA MIT Comp Sci & Artificial Intelligence Lab Cambridge MA USA

ISBN: (纸本)9783031474002;9783031474019

Most state-of-the-art techniques for medical image segmentation rely on deep-learning models. these models, however, are often trained on narrowly-defined tasks in a supervised fashion, which requires expensive labeled datasets. Recent advances in several machine learning domains, such as natural language generation have demonstrated the feasibility and utility of building foundation models that can be customized for various downstream tasks with little to no labeled data. this likely represents a paradigm shift for medical imaging, where we expect that foundation models may shape the future of the field. In this paper, we consider a recently developed foundation model for medical image segmentation, UniverSeg [6]. We conduct an empirical evaluation study in the context of prostate imaging and compare it against the conventional approach of training a task-specific segmentation model. Our results and discussion highlight several important factors that will likely be important in the development and adoption of foundation models for medical image segmentation.

关键词： Foundation model medical image Segmentation Prostate MRI In-context Learning

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GPT4MIA: Utilizing Generative Pre-trained Transformer (GPT-3) as a Plug-and-Play Transductive Model for medical image Analysis 26th

GPT4MIA: Utilizing Generative Pre-trained Transformer (GPT-3...

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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： Zhang, Yizhe Chen, Danny Z. Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

ISBN: (纸本)9783031474002;9783031474019

In this paper, we propose a novel approach (called GPT4MIA) that utilizes Generative Pre-trained Transformer (GPT) as a plug-and-play transductive inference tool for medical image analysis (MIA). We provide theoretical analysis on why a large pre-trained language model such as GPT-3 can be used as a plug-and-play transductive inference model for MIA. At the methodological level, we develop several technical treatments to improve the efficiency and effectiveness of GPT4MIA, including better prompt structure design, sample selection, and prompt ordering of representative samples/features. We present two concrete use cases (with workflow) of GPT4MIA: (1) detecting prediction errors and (2) improving prediction accuracy, working in conjecture with well-established vision-based models for image classification (e.g., ResNet). Experiments validate that our proposed method is effective for these two tasks. We further discuss the opportunities and challenges in utilizing Transformer-based large language models for broader MIA applications.

关键词： medical image Classification Generative Pre-trained Transformer GPT-3 Large Language Models Transductive Inference

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Semantic image Synthesis for Abdominal CT 3rd

Semantic Image Synthesis for Abdominal CT

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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： Zhuang, Yan Hou, Benjamin Mathai, Tejas Sudharshan Mukherjee, Pritam Kim, Boah Summers, Ronald M. NIH Imaging Biomarkers & Comp Aided Diag Lab Dept Radiol & Imaging Sci Ctr Clin Bethesda MD 20892 USA

ISBN: (纸本)9783031537660;9783031537677

As a new emerging and promising type of generative models, diffusion models have proven to outperform Generative Adversarial Networks (GANs) in multiple tasks, including image synthesis. In this work, we explore semantic image synthesis for abdominal CT using conditional diffusion models, which can be used for downstream applications such as data augmentation. We systematically evaluated the performance of three diffusion models, as well as to other state-of-the-art GAN-based approaches, and studied the different conditioning scenarios for the semantic mask. Experimental results demonstrated that diffusion models were able to synthesize abdominal CT images with better quality. Additionally, encoding the mask and the input separately is more effective than naive concatenating.

关键词： CT Abdomen Diffusion model Semantic image Synthesis

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4th international Workshop on Imaging and Treatment Challenges, SWITCH 2024, and 6th international Challenge on Ischemic Stroke Lesion Segmentation Challenge, ISLES 2024, Held in Conjunction with medical image computing and computer assisted intervention, MICCAI 2024

4th International Workshop on Imaging and Treatment Challeng...

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4th international Workshop on Imaging and Treatment Challenges, SWITCH 2024, and 6th international Challenge on Ischemic Stroke Lesion Segmentation Challenge, ISLES 2024, Held in Conjunction with medical image computing and computer assisted intervention, MICCAI 2024

ISBN: (纸本)9783031811005

the proceedings contain 12 papers. the special focus in this conference is on Imaging and Treatment Challenges and international Challenge on Ischemic Stroke Lesion Segmentation Challenge, Held in Conjunction with medical image computing and computer assisted intervention. the topics include: Bayesian Uncertainty Estimation Improves nnU-Net Generalization to Unseen Sites for Stroke Lesion Segmentation;Usefulness of Monoenergetic Non-contrast CT and X-Map images for Deep Learning-Based Stroke Lesion Segmentation;improved Stroke Lesion Segmentation via Cross-Model Knowledge Distillation;Virtual DSA for Learning Contrast Agent Dynamics in Projection Space;robust Feature Selection for Classifying Early Ischemic Changes in Posterior Stroke;ArterialGNet: Impossible Femoral Access Prediction in Stroke Mechanical thrombectomy with Vascular Centerline Graph Embeddings;Spatio-Temporal Deep Learning for Final Infarct Prediction Using Acute Stroke CT Perfusion Data;Utilizing Baseline Infarct and Penumbra Masks to Improve NCCT and CTA Based Final Infarct Prediction;final Stroke Infarct Segmentation Using Deep Neural Networks;A Multi-modal Deep Learning Framework for Final Infarct Prediction in Acute Ischemic Stroke: Combining CTA, NCCT, and Clinical Data.

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AViT: Adapting Vision Transformers for Small Skin Lesion Segmentation Datasets 26th

AViT: Adapting Vision Transformers for Small Skin Lesion Seg...

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

ISBN: (纸本)9783031474002;9783031474019

Skin lesion segmentation (SLS) plays an important role in skin lesion analysis. Vision transformers (ViTs) are considered an auspicious solution for SLS, but they require more training data compared to convolutional neural networks (CNNs) due to their inherent parameterheavy structure and lack of some inductive biases. To alleviate this issue, current approaches fine-tune pre-trained ViT backbones on SLS datasets, aiming to leverage the knowledge learned from a larger set of natural images to lower the amount of skin training data needed. However, fully fine-tuning all parameters of large backbones is computationally expensive and memory intensive. In this paper, we propose AViT, a novel efficient strategy to mitigate ViTs' data-hunger by transferring any pretrained ViTs to the SLS task. Specifically, we integrate lightweight modules (adapters) within the transformer layers, which modulate the feature representation of a ViT without updating its pre-trained weights. In addition, we employ a shallow CNN as a prompt generator to create a prompt embedding from the input image, which grasps fine-grained information and CNN's inductive biases to guide the segmentation task on small datasets. Our quantitative experiments on 4 skin lesion datasets demonstrate that AViT achieves competitive, and at times superior, performance to SOTA but with significantly fewer trainable parameters. Our code is available at https://***/siyi-wind/AViT.

关键词： Vision Transformer Data-efficiency Efficiency medical image Segmentation Dermatology

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Federated Model Aggregation via Self-supervised Priors for Highly Imbalanced medical image Classification 26th

Federated Model Aggregation via Self-supervised Priors for H...

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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： Elbatel, Marawan Wang, Hualiang Mart, Robert Fu, Huazhu Li, Xiaomeng Hong Kong Univ Sci & Technol Hong Kong Peoples R China Univ Girona Comp Vis & Robot Inst Girona Spain ASTAR Inst High Performance Comp IHPC Singapore Singapore

ISBN: (纸本)9783031474002;9783031474019

In the medical field, federated learning commonly deals with highly imbalanced datasets, including skin lesions and gastrointestinal images. Existing federated methods under highly imbalanced datasets primarily focus on optimizing a global model without incorporating the intra-class variations that can arise in medical imaging due to different populations, findings, and scanners. In this paper, we study the interclient intra-class variations with publicly available self-supervised auxiliary networks. Specifically, we find that employing a shared auxiliary pre-trained model, like MoCo-V2, locally on every client yields consistent divergence measurements. Based on these findings, we derive a dynamic balanced model aggregation via self-supervised priors (MAS) to guide the global model optimization. Fed-MAS can be utilized with different local learning methods for effective model aggregation toward a highly robust and unbiased global model. Our code is available at https://***/xmed-lab/Fed-MAS.

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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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Segmentation Style Discovery: Application to Skin Lesion images 9th

Segmentation Style Discovery: Application to Skin Lesion Ima...

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

作者： Abhishek, Kumar Kawahara, Jeremy Hamarneh, Ghassan Simon Fraser Univ Sch Comp Sci Burnaby BC Canada AIP Labs Budapest Hungary

ISBN: (纸本)9783031776090;9783031776106

Variability in medical image segmentation, arising from annotator preferences, expertise, and their choice of tools, has been well documented. While the majority of multi-annotator segmentation approaches focus on modeling annotator-specific preferences, they require annotator-segmentation correspondence. In this work, we introduce the problem of segmentation style discovery, and propose StyleSeg, a segmentation method that learns plausible, diverse, and semantically consistent segmentation styles from a corpus of image-mask pairs without any knowledge of annotator correspondence. StyleSeg consistently outperforms competing methods on four publicly available skin lesion segmentation (SLS) datasets. We also curate ISIC-MultiAnnot, the largest multi-annotator SLS dataset with annotator correspondence, and our results show a strong alignment, using our newly proposed measure AS(2), between the predicted styles and annotator preferences. the code and the dataset are available at https://***/sfu-mial/StyleSeg.

关键词： inter-rater variability image segmentation dermatology

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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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