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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151-160 订阅

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S3M: Scalable Statistical Shape Modeling Through Unsupervised Correspondences 26th

S3M: Scalable Statistical Shape Modeling Through Unsupervise...

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

作者： Bastian, Lennart Baumann, Alexander Hoppe, Emily Buergin, Vincent Kim, Ha Young Saleh, Mahdi Busam, Benjamin Navab, Nassir Tech Univ Munich Comp Aided Med Procedures Munich Germany

ISBN: (纸本)9783031439988;9783031439995

Statistical shape models (SSMs) are an established way to represent the anatomy of a population with various clinically relevant applications. However, they typically require domain expertise, and labor-intensive landmark annotations to construct. We address these shortcomings by proposing an unsupervised method that leverages deep geometric features and functional correspondences to simultaneously learn local and global shape structures across population anatomies. Our pipeline significantly improves unsupervised correspondence estimation for SSMs compared to baseline methods, even on highly irregular surface topologies. We demonstrate this for two different anatomical structures: the thyroid and a multi-chamber heart dataset. Furthermore, our method is robust enough to learn from noisy neural network predictions, potentially enabling scaling SSMs to larger patient populations without manual segmentation annotation. The code is publically available at: https://***/alexanderbaumann99/S3M

关键词： Statistical Shape Modeling Unsupervised Correspondence Estimation Geometric Deep Learning

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Multi-scale Prototypical Transformer forWhole Slide image Classification 26th

Multi-scale Prototypical Transformer forWhole Slide Image Cl...

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

作者： Ding, Saisai Wang, Jun Li, Juncheng Shi, Jun Shanghai Univ Sch Commun & Informat Engn Shanghai Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Whole slide image (WSI) classification is an essential task in computational pathology. Despite the recent advances in multiple instance learning (MIL) for WSI classification, accurate classification of WSIs remains challenging due to the extreme imbalance between the positive and negative instances in bags, and the complicated pre-processing to fuse multi-scale information ofWSI. To this end, we propose a novel multi-scale prototypical Transformer (MSPT) for WSI classification, which includes a prototypical Transformer (PT) module and a multi-scale feature fusion module (MFFM). The PT is developed to reduce redundant instances in bags by integrating prototypical learning into the Transformer architecture. It substitutes all instances with cluster prototypes, which are then re-calibrated through the self-attention mechanism of Transformer. Thereafter, an MFFM is proposed to fuse the clustered prototypes of different scales, which employs MLP-Mixer to enhance the information communication between prototypes. The experimental results on two public WSI datasets demonstrate that the pro-posed MSPT outperforms all the compared algorithms, suggesting its potential applications.

关键词： Whole slide image Multiple instance learning Multi-scale feature Prototypical Transformer

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Conditional Diffusion Models for Weakly Supervised medical image Segmentation 26th

Conditional Diffusion Models for Weakly Supervised Medical I...

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

作者： Hu, Xinrong Chen, Yu-Jen Ho, Tsung-Yi Shi, Yiyu Univ Notre Dame Notre Dame IN 46556 USA Natl Tsing Hua Univ Hsinchu Taiwan Chinese Univ Hong Kong Shatin Hong Kong Peoples R China

ISBN: (纸本)9783031439001;9783031439018

Recent advances in denoising diffusion probabilistic models have shown great success in image synthesis tasks. While there are already works exploring the potential of this powerful tool in image semantic segmentation, its application in weakly supervised semantic segmentation (WSSS) remains relatively under-explored. Observing that conditional diffusion models (CDM) is capable of generating images subject to specific distributions, in this work, we utilize category-aware semantic information underlied in CDM to get the prediction mask of the target object with only image-level annotations. More specifically, we locate the desired class by approximating the derivative of the output of CDM w.r.t the input condition. Our method is different from previous diffusion model methods with guidance from an external classifier, which accumulates noises in the background during the reconstruction process. Our method outperforms state-of-the-art CAM and diffusion model methods on two public medical image segmentation datasets, which demonstrates that CDM is a promising tool in WSSS. Also, experiment shows our method is more time-efficient than existing diffusion model methods, making it practical for wider applications. The codes are available at https://***/xhu248/cond_ddpm_wsss.

关键词： weakly supervised semantic segmentation diffusion models brain tumor magnetic resonance imaging

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DHC: Dual-Debiased Heterogeneous Co-training Framework for Class-Imbalanced Semi-supervised medical image Segmentation 1

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

作者： Wang, Haonan Li, Xiaomeng Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

The volume-wise labeling of 3D medical images is expertise-demanded and time-consuming;hence semi-supervised learning (SSL) is highly desirable for training with limited labeled data. Imbalanced class distribution is a severe problem that bottlenecks the real-world application of these methods but was not addressed much. Aiming to solve this issue, we present a novel Dual-debiased Heterogeneous Cotraining (DHC) framework for semi-supervised 3D medical image segmentation. Specifically, we propose two loss weighting strategies, namely Distribution-aware Debiased Weighting (DistDW) and Difficulty-aware Debiased Weighting (DiffDW), which leverage the pseudo labels dynamically to guide the model to solve data and learning biases. The framework improves significantly by co-training these two diverse and accurate sub-models. We also introduce more representative benchmarks for classim-balanced semi-supervised medical image segmentation, which can fully demonstrate the efficacy of the class-imbalance designs. Experiments show that our proposed framework brings significant improvements by using pseudo labels for debiasing and alleviating the class imbalance problem. More importantly, our method outperforms the state-of-theart SSL methods, demonstrating the potential of our framework for the more challenging SSL setting. Code and models are available at: https://***/xmed-lab/DHC.

关键词： Semi-supervised learning Class imbalance 3D medical image segmentation CT image

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NASDM: Nuclei-Aware Semantic Histopathology image Generation Using Diffusion Models 26th

NASDM: Nuclei-Aware Semantic Histopathology Image Generation...

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

作者： Shrivastava, Aman Fletcher, P. Thomas Univ Virginia Charlottesville VA 22903 USA

ISBN: (纸本)9783031439865;9783031439872

In recent years, computational pathology has seen tremendous progress driven by deep learning methods in segmentation and classification tasks aiding prognostic and diagnostic settings. Nuclei segmentation, for instance, is an important task for diagnosing different cancers. However, training deep learning models for nuclei segmentation requires large amounts of annotated data, which is expensive to collect and label. This necessitates explorations into generative modeling of histopathological images. In this work, we use recent advances in conditional diffusion modeling to formulate a first-of-its-kind nuclei-aware semantic tissue generation framework (NASDM) which can synthesize realistic tissue samples given a semantic instance mask of up to six different nuclei types, enabling pixel-perfect nuclei localization in generated samples. These synthetic images are useful in applications in pathology pedagogy, validation of models, and supplementation of existing nuclei segmentation datasets. We demonstrate that NASDM is able to synthesize high-quality histopathology images of the colon with superior quality and semantic controllability over existing generative methods. Implementation: https://***/4m4n5/NASDM.

关键词： Generative Modeling Histopathology Diffusion Models

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11th Workshop on Clinical image-Based Procedures, CLIP 2022, held in conjunction with the 25th international conference on medical image computing and computer assisted intervention, MICCAI 2022

11th Workshop on Clinical Image-Based Procedures, CLIP 2022,...

引用

11th Workshop on Clinical image-Based Procedures, CLIP 2022, held in conjunction with the 25th international conference on medical image computing and computer assisted intervention, MICCAI 2022

ISBN: (纸本)9783031231780

The proceedings contain 9 papers. The special focus in this conference is on Clinical image-Based Procedures. The topics include: Machine Learning Based Approach for Motion Detection and Estimation in Routinely Acquired Low Resolution Near Infrared Fluorescence Optical Imaging;automatic Landmark Identification on IntraOralScans;STAU-Net: A Spatial Structure Attention Network for 3D Coronary Artery Segmentation;convolutional Redistribution Network for Multi-view medical image Diagnosis;feature Patch Based Attention Model for Dental Caries Classification;Conditional Domain Adaptation Based on Initial Distribution Discrepancy for EEG Emotion Recognition;preface.

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Semi-supervised Domain Adaptive medical image Segmentation Through Consistency Regularized Disentangled Contrastive Learning 26th

Semi-supervised Domain Adaptive Medical Image Segmentation T...

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

作者： Basak, Hritam Yin, Zhaozheng SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA

ISBN: (纸本)9783031439001;9783031439018

Although unsupervised domain adaptation (UDA) is a promising direction to alleviate domain shift, they fall short of their supervised counterparts. In this work, we investigate relatively less explored semi-supervised domain adaptation (SSDA) for medical image segmentation, where access to a few labeled target samples can improve the adaptation performance substantially. Specifically, we propose a two-stage training process. First, an encoder is pre-trained in a self-learning paradigm using a novel domain-content disentangled contrastive learning (CL) along with a pixel-level feature consistency constraint. The proposed CL enforces the encoder to learn discriminative content-specific but domain-invariant semantics on a global scale from the source and target images, whereas consistency regularization enforces the mining of local pixel-level information by maintaining spatial sensitivity. This pre-trained encoder, along with a decoder, is further fine-tuned for the downstream task, (i.e. pixel-level segmentation) using a semi-supervised setting. Furthermore, we experimentally validate that our proposed method can easily be extended for UDA settings, adding to the superiority of the proposed strategy. Upon evaluation on two domain adaptive image segmentation tasks, our proposed method outperforms the SoTA methods, both in SSDA and UDA settings. Code is available at GitHub.

关键词： Contrastive Learning Style-content disentanglement Consistency Regularization Domain Adaptation Segmentation

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SplitFed Resilience to Packet Loss: Where to Split, that is the Question 26th

SplitFed Resilience to Packet Loss: Where to Split, that is ...

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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： Shiranthika, Chamani Kafshgari, Zahra Hafezi Saeedi, Parvaneh Bajic, Ivan V. Simon Fraser Univ Sch Engn Sci Burnaby BC Canada

ISBN: (纸本)9783031474002;9783031474019

Decentralized machine learning has broadened its scope recently with the invention of Federated Learning (FL), Split Learning (SL), and their hybrids like Split Federated Learning (SplitFed or SFL). The goal of SFL is to reduce the computational power required by each client in FL and parallelize SL while maintaining privacy. This paper investigates the robustness of SFL against packet loss on communication links. The performance of various SFL aggregation strategies is examined by splitting the model at two points - shallow split and deep split - and testing whether the split point makes a statistically significant difference to the accuracy of the final model. Experiments are carried out on a segmentation model for human embryo images and indicate the statistically significant advantage of a deeper split point.

关键词： SplitFed Learning packet loss human embryo image segmentation

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A2FSeg: Adaptive Multi-modal Fusion Network for medical image Segmentation 26th

A2FSeg: Adaptive Multi-modal Fusion Network for Medical Imag...

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

作者： Wang, Zirui Hong, Yi Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9783031439001;9783031439018

Magnetic Resonance Imaging (MRI) plays an important role in multi-modal brain tumor segmentation. However, missing modality is very common in clinical diagnosis, which will lead to severe segmentation performance degradation. In this paper, we propose a simple adaptive multi-modal fusion network for brain tumor segmentation, which has two stages of feature fusion, including a simple average fusion and an adaptive fusion based on an attention mechanism. Both fusion techniques are capable to handle the missing modality situation and contribute to the improvement of segmentation results, especially the adaptive one. We evaluate our method on the BraTS2020 dataset, achieving the state-of-the-art performance for the incomplete multi-modal brain tumor segmentation, compared to four recent methods. Our A2FSeg (Average and Adaptive Fusion Segmentation network) is simple yet effective and has the capability of handling any number of image modalities for incomplete multi-modal segmentation. Our source code is online and available at https://***/Zirui0623/***.

关键词： Modality-adaptive fusion Missing modality Brain tumor segmentation Incomplete multi-modal segmentation

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An Ensemble of Machine Learning Models Utilizing Deep Convolutional Features for medical image Classification 3rd

An Ensemble of Machine Learning Models Utilizing Deep Convol...

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3rd international conference on Advanced Network Technologies and Intelligent computing (ANTIC)

作者： Jana, Nanda Dulal Dhar, Sandipan Ghosh, Subhayu Phukan, Sukonya Gogoi, Rajlakshmi Singh, Jyoti Natl Inst Technol Durgapur Durgapur 713209 India Jorhat Engn Coll Jorhat 785007 Assam India

ISBN: (纸本)9783031640698;9783031640704

medical image classification is a rapidly growing research field that has revolutionised various diseases' traditional diagnosis, treatment planning, and prognosis prediction. Due to the recent advancements in deep learning (DL) algorithms for medical imaging, the ability to identify anomalies and crucial features in medical images has greatly improved, enhancing the precision of diagnosis and treatment. However, the training process of deep learning algorithms is time-consuming and resource-intensive. In contrast, a lightweight model can be easily deployed for real-time implementation. Furthermore, in preceding studies, single models are primarily deployed for performing medical image classification tasks, though an ensemble of various models can enhance overall performance. Therefore, in this work, an ensemble of six machine learning (ML) models is used to reduce the training time compared to a conventional DL model. Moreover, the features considered in this work are deep convolutional features extracted using pre-trained DL models. The dataset considered in this work includes X-rays, CT scans, and MRI images of human body parts associated with several diseases. Our proposed ensemble learning approach is experimentally proven to outperform individual models, considering the deep convolutional features.

关键词： medical image Classification Ensemble Learning Approach Deep Convolutional Features

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