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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151-160 订阅

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5th international MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the medical image computing for computer assisted intervention, MICCAI 2019

5th International MICCAI Brainlesion Workshop, BrainLes 2019...

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5th international MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the medical image computing for computer assisted intervention, MICCAI 2019

ISBN: (纸本)9783030466428

the proceedings contain 75 papers. the special focus in this conference is on Brainlesion Workshop. the topics include: 3D automatic brain tumor segmentation using a multiscale input U-Net network;semi-supervised variational autoencoder for survival prediction;multi-modal U-Nets with boundary loss and pre-training for brain tumor segmentation;multidimensional and multiresolution ensemble networks for brain tumor segmentation;hybrid labels for brain tumor segmentation;Two stages CNN-Based segmentation of gliomas, uncertainty quantification and prediction of overall patient survival;Detection and segmentation of brain tumors from MRI using U-Nets;Multimodal segmentation with MGF-Net and the focal tversky loss function;brain tumor segmentation using 3D convolutional neural network;multimodal brain image segmentation and analysis with neuromorphic attention-based learning;DDU-Nets: Distributed dense model for 3D MRI brain tumor segmentation;brain tumor segmentation based on 3D residual U-Net;Automatic segmentation of brain tumor from 3D MR images using SegNet, U-Net, and PSP-Net;3D deep residual encoder-decoder CNNS with squeeze-and-excitation for brain tumor segmentation;Overall survival prediction using conventional MRI features;a multi-path decoder network for brain tumor segmentation;the tumor Mix-Up in 3D unet for glioma segmentation;multi-branch learning framework with different receptive fields ensemble for brain tumor segmentation;domain knowledge based brain tumor segmentation and overall survival prediction;Encoder-decoder network for brain tumor segmentation on multi-sequence MRI;Improving brain tumor segmentation in multi-sequence MR images using cross-sequence MR image generation;deep convolutional neural networks for brain tumor segmentation: Boosting performance using deep transfer learning: Preliminary results;multimodal brain tumor segmentation with normal appearance autoencoder.

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Concept Bottleneck with Visual Concept Filtering for Explainable medical image Classification 26th

Concept Bottleneck with Visual Concept Filtering for Explain...

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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, Injae Kim, Jongha Choi, Joonmyung Kim, Hyunwoo J. Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9783031474002;9783031474019

Interpretability is a crucial factor in building reliable models for various medical applications. Concept Bottleneck Models (CBMs) enable interpretable image classification by utilizing human-understandable concepts as intermediate targets. Unlike conventional methods that require extensive human labor to construct the concept set, recent works leveraging Large Language Models (LLMs) for generating concepts made automatic concept generation possible. However, those methods do not consider whether a concept is visually relevant or not, which is an important factor in computing meaningful concept scores. therefore, we propose a visual activation score that measures whether the concept contains visual cues or not, which can be easily computed with unlabeled image data. Computed visual activation scores are then used to filter out the less visible concepts, thus resulting in a final concept set with visually meaningful concepts. Our experimental results show that adopting the proposed visual activation score for concept filtering consistently boosts performance compared to the baseline. Moreover, qualitative analyses also validate that visually relevant concepts are successfully selected with the visual activation score.

关键词： medical image Classification Explainable AI Concept Bottleneck Models Large Language Models

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

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

引用

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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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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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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Cross-Adversarial Local Distribution Regularization for Semi-supervised medical image Segmentation 26th

Cross-Adversarial Local Distribution Regularization for Semi...

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

作者： thanh Nguyen-Duc Trung Le Bammer, Roland Zhao, He Cai, Jianfei Dinh Phung Monash Univ Melbourne Australia CSIROs Data61 Melbourne Australia

ISBN: (纸本)9783031439063;9783031439070

medical semi-supervised segmentation is a technique where a model is trained to segment objects of interest in medical images with limited annotated data. Existing semi-supervised segmentation methods are usually based on the smoothness assumption. this assumption implies that the model output distributions of two similar data samples are encouraged to be invariant. In other words, the smoothness assumption states that similar samples (e.g., adding small perturbations to an image) should have similar outputs. In this paper, we introduce a novel cross-adversarial local distribution (Cross-ALD) regularization to further enhance the smoothness assumption for semi-supervised medical image segmentation task. We conducted comprehensive experiments that the Cross-ALD archives state-of-the-art performance against many recent methods on the public LA and ACDC datasets.

关键词： Semi-supervised segmentation Adversarial local distribution Adversarial examples Cross-adversarial local distribution

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Structure-Preserving Instance Segmentation via Skeleton-Aware Distance Transform 1

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

作者： Lin, Zudi Wei, Donglai Gupta, Aarush Liu, Xingyu Sun, Deqing Pfister, Hanspeter Harvard Univ Cambridge MA 02138 USA Boston Coll Chestnut Hill MA 02167 USA CMU Pittsburgh PA USA Google Res Mountain View CA USA

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Objects with complex structures pose significant challenges to existing instance segmentation methods that rely on boundary or affinity maps, which are vulnerable to small errors around contacting pixels that cause noticeable connectivity change. While the distance transform (DT) makes instance interiors and boundaries more distinguishable, it tends to overlook the intra-object connectivity for instances with varying width and result in over-segmentation. To address these challenges, we propose a skeleton-aware distance transform (SDT) that combines the merits of object skeleton in preserving connectivity and DT in modeling geometric arrangement to represent instances with arbitrary structures. Comprehensive experiments on histopathology image segmentation demonstrate that SDT achieves state-of-the-art performance.

关键词： image segmentation

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5th international MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the medical image computing for computer assisted intervention, MICCAI 2019

5th International MICCAI Brainlesion Workshop, BrainLes 2019...

引用

5th international MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the medical image computing for computer assisted intervention, MICCAI 2019

ISBN: (纸本)9783030466398

the proceedings contain 75 papers. the special focus in this conference is on Brainlesion Workshop. the topics include: Saliency based deep neural network for automatic detection of gadolinium-enhancing multiple sclerosis lesions in brain MRI;deep learning for brain tumor segmentation in radiosurgery: prospective clinical evaluation;3d u-net based brain tumor segmentation and survival days prediction;aggregating multi-scale prediction based on 3d u-net in brain tumor segmentation;brain tumor synthetic segmentation in 3d multimodal mri scans;multi-step cascaded networks for brain tumor segmentation;tunet: end-to-end hierarchical brain tumor segmentation using cascaded networks;using separated inputs for multimodal brain tumor segmentation with 3d u-net-like architectures;two-step u-nets for brain tumor segmentation and random forest with radiomics for survival time prediction;Tbi lesion segmentation in head CT: Impact of preprocessing and data augmentation;bag of tricks for 3d mri brain tumor segmentation;multi-resolution 3d cnn for mri brain tumor segmentation and survival prediction;two-stage cascaded u-net: 1st place solution to brats challenge 2019 segmentation task;memory-efficient cascade 3d u-net for brain tumor segmentation;a baseline for predicting glioblastoma patient survival time with classical statistical models and primitive features ignoring image information;brain tumor segmentation and survival prediction using 3d attention unet;brain tumor segmentation using dense channels 2d u-net and multiple feature extraction network;Brain tumour segmentation on MRI images by voxel classification using neural networks, and patient survival prediction;oncohabitats glioma segmentation model;brain tumor segmentation with uncertainty estimation and overall survival prediction;multi-task learning for brain tumor segmentation.

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A Survey on Deep Learning-Based medical image Registration 5th

A Survey on Deep Learning-Based Medical Image Registration

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5th international conference on Neural computing for Advanced Applications (NCAA)

作者： Xu, Ronghao Liu, Chongxin Liu, Shuaitong Huang, Weijie Zhang, Menghua Univ Jinan Sch Elect Engn Jinan 250000 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

In recent years, various methods have been proposed to address the fundamental task of medical image registration in medical image analysis. this paper systematically reviews the research progress in medical image registration using deep learning techniques. these methods utilize deep learning algorithms to achieve precise alignment of medical image data from different modalities, time points, or sources, thereby providing dependable help for clinical identification and care. this paper seeks to provide researchers and practitioners with the most current technological advances and potential applications in the realm of medical image registration.

关键词： image registration Deep learning CNN Supervised Learning

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X2Vision: 3D CT Reconstruction from Biplanar X-Rays with Deep Structure Prior 26th

X2Vision: 3D CT Reconstruction from Biplanar X-Rays with Dee...

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

作者： Cafaro, Alexandre Spinat, Quentin Leroy, Amaury Maury, Pauline Munoz, Alexandre Beldjoudi, Guillaume Robert, Charlotte Deutsch, Eric Gregoire, Vincent Lepetit, Vincent Paragios, Nikos TheraPanacea Paris France Paris Saclay Univ Gustave Roussy Inserm 1030 Villejuif France Ctr Leon Berard Dept Radiat Oncol Lyon France Univ Gustave Eiffel CNRS LIGM Ecole Ponts Paris France

ISBN: (纸本)9783031439988;9783031439995

We propose an unsupervised deep learning method to reconstruct a 3D tomographic image from biplanar X-rays, to reduce the number of required projections, the patient dose, and the acquisition time. To address this ill-posed problem, we introduce prior knowledge of anatomic structures by training a generative model on 3D CTs of head and neck. We optimize the latent vectors of the generative model to recover a volume that both integrates this prior knowledge and ensures consistency between the reconstructed image and input projections. Our method outperforms recent methods in terms of reconstruction error while being faster and less radiating than current clinical workflow. We evaluate our method in a clinical configuration for radiotherapy.

关键词： image reconstruction Inverse problem Sparse sampling Deep generative model CT

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