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

共 2694 条记录，以下是301-310 订阅

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MulHiST: Multiple Histological Staining for thick Biological Samples via Unsupervised image-to-image Translation 26th

MulHiST: Multiple Histological Staining for Thick Biological...

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

作者： Shi, Lulin Zhang, Yan Wong, Ivy H. M. Lo, Claudia T. K. Wong, Terence T. W. Hong Kong Univ Sci & Technol Dept Chem & Biol Engn Translat & Adv Bioimaging Lab Hong Kong Peoples R China

ISBN: (纸本)9783031439865;9783031439872

the conventional histopathology paradigm can provide the gold standard for clinical diagnosis, which, however, suffers from lengthy processing time and requires costly laboratory equipment. Recent advancements made in deep learning for computational histopathology have sparked lots of efforts in achieving a rapid chemical-free staining technique. Yet, existing approaches are limited to well-prepared thin sections, and invalid in handling more than one stain. In this paper, we present a multiple histological staining model for thick tissues (MulHiST), without any laborious sample preparation, sectioning, and staining process. We use the grey-scale light-sheet microscopy image of thick tissues as model input and transfer it into different histologically stained versions, including hematoxylin and eosin (H&E), Masson's trichrome (MT), and periodic acid-Schiff (PAS). this is the first work that enables the automatic and simultaneous generation of multiple histological staining for thick biological samples. Moreover, we empirically demonstrate that the AdaIN-based generator offers an advantage over other configurations to achieve higher-quality multi-style image generation. Extensive experiments also indicated that multi-domain data fusion is conducive to the model capturing shared pathological features. We believe that the proposed MulHiST can potentially be applied in clinical rapid pathology and will significantly improve the current histological workflow.

关键词： Virtual staining Multi-domain image translation thick tissues Light-sheet microscopy

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Client-Level Differential Privacy via Adaptive Intermediary in Federated medical Imaging 26th

Client-Level Differential Privacy via Adaptive Intermediary ...

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

作者： Jiang, Meirui Zhong, Yuan Le, Anjie Li, Xiaoxiao Dou, Qi Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada

ISBN: (纸本)9783031438943;9783031438950

Despite recent progress in enhancing the privacy of federated learning (FL) via differential privacy (DP), the trade-off of DP between privacy protection and performance is still underexplored for real-world medical scenario. In this paper, we propose to optimize the trade-off under the context of client-level DP, which focuses on privacy during communications. However, FL for medical imaging involves typically much fewer participants (hospitals) than other domains (e.g., mobile devices), thus ensuring clients be differentially private is much more challenging. To tackle this problem, we propose an adaptive intermediary strategy to improve performance without harming privacy. Specifically, we theoretically find splitting clients into sub-clients, which serve as intermediaries between hospitals and the server, can mitigate the noises introduced by DP without harming privacy. Our proposed approach is empirically evaluated on both classification and segmentation tasks using two public datasets, and its effectiveness is demonstrated with significant performance improvements and comprehensive analytical studies. Code is available at: https://***/med-air/Client-DP-FL.

关键词： Federated Learning Client-level Differential Privacy medical image Analysis

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Bi-directional Encoding for Explicit Centerline Segmentation by Fully-Convolutional Networks 25th

Bi-directional Encoding for Explicit Centerline Segmentation...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Sirazitdinov, Ilyas Saalbach, Axel Schulz, Heinrich Dylov, Dmitry V. Philips Res Hamburg Germany Skolkovo Inst Sci & Technol Moscow Russia

ISBN: (纸本)9783031164408;9783031164392

Localization of tube-shaped objects is an important topic in medical imaging. Previously it was mainly addressed via dense segmentation that may produce inconsistent results for long and narrow objects. In our work, we propose a point-based approach for explicit centerline segmentation that can be learned by fully-convolutional networks. We propose a new bi-directional encoding scheme that does not require any autoregressive blocks and is robust to various shapes and orientations of lines, being adaptive to the number of points in their centerlines. We present extensive evaluation of our approach on synthetic and real data (chest x-ray and coronary angiography) and show its advantage over the state-of-the-art segmentation models.

关键词： Tube-shaped segmentation Centerline segmentation Bi-directional encoding computer-assisted intervention Catheter tracking

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Elongated Physiological Structure Segmentation via Spatial and Scale Uncertainty-Aware Network 26th

Elongated Physiological Structure Segmentation via Spatial a...

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

作者： Zhang, Yinglin Xi, Ruiling Fu, Huazhu Towey, Dave Bai, RuiBin Higashita, Risa Liu, Jiang Univ Nottingham Ningbo China Sch Comp Sci Ningbo 315100 Peoples R China Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Tomey Corp Nagoya Aichi 4510051 Japan ASTAR Inst High Performance Comp IHPC Singapore Singapore

ISBN: (纸本)9783031439001;9783031439018

Robust and accurate segmentation for elongated physiological structures is challenging, especially in the ambiguous region, such as the corneal endothelium microscope image with uneven illumination or the fundus image with disease interference. In this paper, we present a spatial and scale uncertainty-aware network (SSU-Net) that fully uses both spatial and scale uncertainty to highlight ambiguous regions and integrate hierarchical structure contexts. First, we estimate epistemic and aleatoric spatial uncertainty maps using Monte Carlo dropout to approximate Bayesian networks. Based on these spatial uncertainty maps, we propose the gated soft uncertainty-aware (GSUA) module to guide the model to focus on ambiguous regions. Second, we extract the uncertainty under different scales and propose the multiscale uncertainty-aware (MSUA) fusion module to integrate structure contexts from hierarchical predictions, strengthening the final prediction. Finally, we visualize the uncertainty map of final prediction, providing interpretability for segmentation results. Experiment results show that the SSU-Net performs best on cornea endothelial cell and retinal vessel segmentation tasks. Moreover, compared with counterpart uncertainty-based methods, SSU-Net is more accurate and robust.

关键词： Uncertainty medical image Segmentation Elongated Physiological Structure Deep Learning

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Semi-supervised Class Imbalanced Deep Learning for Cardiac MRI Segmentation 26th

Semi-supervised Class Imbalanced Deep Learning for Cardiac M...

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

作者： Yuan, Yuchen Wang, Xi Yang, Xikai Li, Ruijiang Heng, Pheng-Ann Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China Stanford Univ Sch Med Dept Radiat Oncol Stanford CA USA Chinese Univ Hong Kong Inst Med Intelligence & XR Shatin Hong Kong Peoples R China

ISBN: (纸本)9783031439001;9783031439018

Despite great progress in semi-supervised learning (SSL) that leverages unlabeled data to improve the performance over fully supervised models, existing SSL approaches still fail to exhibit good results when faced with a severe class imbalance problem in medical image segmentation. In this work, we propose a novel Mean-teacher based class imbalanced learning framework for cardiac magnetic resonance imaging (MRI) segmentation, which can effectively conquer the problems of class imbalance and limited labeled data simultaneously. Specifically, in parallel to the traditional linear-based classifier, we additionally train a prototype-based classifier that makes dense predictions by matching test samples with a set of prototypes. the prototypes are iteratively updated by in-class features encoded in the entire sample set, which can better guide the model training by alleviating the classwise bias exhibited in each individual sample. To reduce the noises in the pseudo labels, we propose a cascaded refining strategy by utilizing two multi-level tree filters that are built upon pairwise pixel similarity in terms of intensity values and semantic features. With the assistance of pixel affinities, soft pseudo labels are properly refined on-the-fly. Upon evaluation on ACDC and MMWHS, two cardiacMRI datasets with prominent class imbalance problem, the proposed method demonstrates the superiority compared to several state-of-the-art methods, especially in the case where few annotations are available (Code is available in https://***/IsYuchenYuan/SSCI).

关键词： Semi-supervised learning medical image segmentation Class imbalance Prototype learning Multi-level tree filters

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Progressive Attention Guidance for Whole Slide Vulvovaginal Candidiasis Screening 26th

Progressive Attention Guidance for Whole Slide Vulvovaginal ...

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

作者： Cai, Jiangdong Xiong, Honglin Cao, Maosong Liu, Luyan Zhang, Lichi Wang, Qian ShanghaiTech Univ Sch Biomed Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Biomed Engn Shanghai Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Vulvovaginal candidiasis (VVC) is the most prevalent human candidal infection, estimated to afflict approximately 75% of all women at least once in their lifetime. It will lead to several symptoms including pruritus, vaginal soreness, and so on. Automatic whole slide image (WSI) classification is highly demanded, for the huge burden of disease control and prevention. However, the WSI-based computer-aided VCC screening method is still vacant due to the scarce labeled data and unique properties of candida. Candida in WSI is challenging to be captured by conventional classification models due to its distinctive elongated shape, the small proportion of their spatial distribution, and the style gap from WSIs. To make the model focus on the candida easier, we propose an attention-guided method, which can obtain a robust diagnosis classification model. Specifically, we first use a pre-trained detection model as prior instruction to initialize the classification model. then we design a Skip Self-Attention module to refine the attention onto the fined-grained features of candida. Finally, we use a contrastive learning method to alleviate the overfitting caused by the style gap of WSIs and suppress the attention to false positive regions. Our experimental results demonstrate that our framework achieves state-of-the-art performance. Code and example data are available at https://***/caijd2000/MICCAI2023-VVC-Screening.

关键词： Whole slide image Vulvovaginal Candidiasis Attention-Guided

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CT-Guided, Unsupervised Super-Resolution Reconstruction of Single 3D Magnetic Resonance image 26th

CT-Guided, Unsupervised Super-Resolution Reconstruction of S...

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

作者： Wang, Jiale Heimann, Alexander F. Tannast, Moritz Zheng, Guoyan Shanghai Jiao Tong Univ Inst Med Robot Sch Biomed Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China Univ Fribourg HFR Cantonal Hosp Dept Orthopaed Surg Fribourg Switzerland

ISBN: (纸本)9783031439063;9783031439070

Deep learning-based algorithms for single MR image (MRI) super-resolution have shown great potential in enhancing the resolution of low-quality images. However, many of these methods rely on supervised training with paired low-resolution (LR) and high-resolution (HR) MR images, which can be difficult to obtain in clinical settings. this is because acquiring HR MR images in clinical settings requires a significant amount of time. In contrast, HR CT images are acquired in clinical routine. In this paper, we propose a CT-guided, unsupervised MRI super-resolution reconstruction method based on joint cross-modality image translation and super-resolution reconstruction, eliminating the requirement of high-resolution MRI for training. the proposed approach is validated on two datasets respectively acquired from two different clinical sites. Well-established metrics including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Metrics (SSIM), and Learned Perceptual image Patch Similarity (LPIPS) are used to assess the performance of the proposed method. Our method achieved an average PSNR of 32.23, an average SSIM of 0.90 and an average LPIPS of 0.14 when evaluated on data of the first site. An average PSNR of 30.58, an average SSIM of 0.88, and an average LPIPS of 0.10 were achieved by our method when evaluated on data of the second site.

关键词： Unsupervised image super-resolution Cross-modality image translation CT-Guided Magnetic resonance imaging

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8th international conference on Information, Communication and computing Technology, ICICCT 2023

8th International Conference on Information, Communication a...

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8th international conference on Information, Communication and computing Technology, ICICCT 2023

ISBN: (纸本)9783031438370

the proceedings contain 14 papers. the special focus in this conference is on international conference on Information, Communication and computing Technology. the topics include: High Speed and Efficient Reconfigurable Histogram Equalisation Architecture for image Contrast Enhancement;A Practical Use for AI-Generated images;homomorphic Encryption Schemes Using Nested Matrices;a Deep Learning Model for Heterogeneous Dataset Analysis - Application to Winter Wheat Crop Yield Prediction;An ARIMA and XGBoost Model Utilized for Forecasting Municipal Solid Waste Generation;an Empirical Study of Intrusion Detection by Combining Clustering and Classification Methods;personalized Movie Recommendation Prediction Using Reinforcement Learning;an Ensemble and Deep Neural Network Based Approaches for Automated Sentiment Analysis;In-store Product Placement Using LiDAR-assisted Discrete PSO Algorithm;deep Residual Variational Autoencoder for image Super-Resolution;Hybrid HAN Model to Investigate Depression from Twitter Posts;transfer Learning-Based Encoder-Decoder Model for Skin Lesion Segmentation.

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Adaptive Supervised PatchNCE Loss for Learning H&E-to-IHC Stain Translation with Inconsistent Groundtruth image Pairs 26th

Adaptive Supervised PatchNCE Loss for Learning H&E-to-IHC St...

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

作者： Li, Fangda Hu, Zhiqiang Chen, Wen Kak, Avinash Purdue Univ W Lafayette IN 47907 USA Sensetime Res Beijing Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Immunohistochemical (IHC) staining highlights the molecular information critical to diagnostics in tissue samples. However, compared to H&E staining, IHC staining can be much more expensive in terms of both labor and the laboratory equipment required. this motivates recent research that demonstrates that the correlations between the morphological information present in the H&E-stained slides and the molecular information in the IHC-stained slides can be used for H&E-to-IHC stain translation. However, due to a lack of pixel-perfect H&E-IHC groundtruth pairs, most existing methods have resorted to relying on expert annotations. To remedy this situation, we present a new loss function, Adaptive Supervised PatchNCE (ASP), to directly deal with the input to target inconsistencies in a proposed H&E-to-IHC image-to-image translation framework. the ASP loss is built upon a patch-based contrastive learning criterion, named Supervised PatchNCE (SP), and augments it further with weight scheduling to mitigate the negative impact of noisy supervision. Lastly, we introduce the Multi-IHC Stain Translation (MIST) dataset, which contains aligned H&E-IHC patches for 4 different IHC stains critical to breast cancer diagnosis. In our experiment, we demonstrate that our proposed method outperforms existing image-to-image translation methods for stain translation to multiple IHC stains. All of our code and datasets are available at https://***/lifangda01/AdaptiveSupervisedPatchNCE.

关键词： Generative Adversarial Network Contrastive Learning H&E-to-IHC Stain Translation

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VISA-FSS: A Volume-Informed Self Supervised Approach for Few-Shot 3D Segmentation 26th

VISA-FSS: A Volume-Informed Self Supervised Approach for Few...

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

作者： Mozafari, Mohammad Bitarafan, Adeleh Azampour, Mohammad Farid Farshad, Azade Baghshah, Mahdieh Soleymani Navab, Nassir Sharif Univ Technol Tehran Iran Tech Univ Munich Comp Aided Med Proc Munich Germany

ISBN: (纸本)9783031438943;9783031438950

Few-shot segmentation (FSS) models have gained popularity in medical imaging analysis due to their ability to generalize well to unseen classes with only a small amount of annotated data. A key requirement for the success of FSS models is a diverse set of annotated classes as the base training tasks. this is a difficult condition to meet in the medical domain due to the lack of annotations, especially in volumetric images. To tackle this problem, self-supervised FSS methods for 3D images have been introduced. However, existing methods often ignore intra-volume information in 3D image segmentation, which can limit their performance. To address this issue, we propose a novel self-supervised volume-aware FSS framework for 3D medical images, termed VISA-FSS. In general, VISA-FSS aims to learn continuous shape changes that exist among consecutive slices within a volumetric image to improve the performance of 3D medical segmentation. To achieve this goal, we introduce a volume-aware task generation method that utilizes consecutive slices within a 3D image to construct more varied and realistic self-supervised FSS tasks during training. In addition, to provide pseudo-labels for consecutive slices, a novel strategy is proposed that propagates pseudo-labels of a slice to its adjacent slices using flow field vectors to preserve anatomical shape continuity. In the inference time, we then introduce a volumetric segmentation strategy to fully exploit the interslice information within volumetric images. Comprehensive experiments on two common medical benchmarks, including abdomen CT and MRI, demonstrate the effectiveness of our model over state-of-the-art methods. Code is available at https://***/sharif- ml- lab/visa- fss

关键词： medical image segmentation Few-shot learning Few-shot semantic segmentation Self-supervised learning Supervoxels

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