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检索条件"任意字段=5th International Conference on Medical Image Computing and Computer-Assisted Intervention"

共 2882 条记录，以下是401-410 订阅

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Attention-Based Recurrent Neural Networks for medical image Classification 5th

Attention-Based Recurrent Neural Networks for Medical Image ...

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Manjunath, K.V. Alam, Intekhab Nidhya, M.S. Verma, Tushar K. School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

Attention-primarily based Recurrent Neural Networks (RNNs) are a technique for medical image classification. RNNs can robotically extract temporal and spatial facts from scientific imaging statistics and make sensible decisions. RNNs are especially nicely-perfect for clinical photograph category obligations, cutting-edge their potential to perceive lengthy-term temporal styles, recognize the relationships between distinct medical picture datasets, and enable In this paper, we overview the existing tries to use interest-based RNNs for medical image classification, discuss the contemporary demanding situations and suggest some ability future guidelines for studies. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

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Scaling up 3D Kernels with Bayesian Frequency Re-parameterization for medical image Segmentation 26th

Scaling up 3D Kernels with Bayesian Frequency Re-parameteriz...

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

作者： Lee, Ho Hin Liu, Quan Bao, Shunxing Yang, Qi Yu, Xin Cai, Leon Y. Li, thomas Z. Huo, Yuankai Koutsoukos, Xenofon Landman, Bennett A. Vanderbilt Univ Dept Comp Sci Nashville TN 37212 USA Vanderbilt Univ Dept Elect & Comp Engn Nashville TN 37212 USA Vanderbilt Univ Dept Biomed Engn Nashville TN 37212 USA

ISBN: (纸本)9783031439001;9783031439018

With the inspiration of vision transformers, the concept of depth-wise convolution revisits to provide a large Effective Receptive Field (ERF) using Large Kernel (LK) sizes for medical image segmentation. However, the segmentation performance might be saturated and even degraded as the kernel sizes scaled up (e.g., 21 x 21 x 21) in a Convolutional Neural Network (CNN). We hypothesize that convolution with LK sizes is limited to maintain an optimal convergence for locality learning. While Structural Re-parameterization (SR) enhances the local convergence with small kernels in parallel, optimal small kernel branches may hinder the computational efficiency for training. In this work, we propose RepUX-Net, a pure CNN architecture with a simple large kernel block design, which competes favorably with current network state-of-the-art (SOTA) (e.g., 3D UX-Net, SwinUNETR) using 6 challenging public datasets. We derive an equivalency between kernel reparameterization and the branch-wise variation in kernel convergence. Inspired by the spatial frequency in the human visual system, we extend to vary the kernel convergence into element-wise setting and model the spatial frequency as a Bayesian prior to reparameterize convolutional weights during training. Specifically, a reciprocal function is leveraged to estimate a frequency-weighted value, which rescales the corresponding kernel element for stochastic gradient descent. From the experimental results, RepUX-Net consistently outperforms 3D SOTA benchmarks with internal validation (FLARE: 0.929 to 0.944), external validation (MSD: 0.901 to 0.932, KiTS: 0.815 to 0.847, LiTS: 0.933 to 0.949, TCIA: 0.736 to 0.779) and transfer learning (AMOS: 0.880 to 0.911) scenarios in Dice Score. Both codes and pre-trained models are available at: https://***/MASILab/RepUX-Net.

关键词： Bayesian Frequency Re-parameterization Large Kernel Convolution medical image Segmentation

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Deployment of image Analysis Algorithms Under Prevalence Shifts 1

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

作者： Godau, Patrick Kalinowski, Piotr Christodoulou, Evangelia Reinke, Annika Tizabi, Minu Ferrer, Luciana Jaeger, Paul F. Maier-Hein, Lena German Canc Res Ctr Div Intelligent Med Syst IMSY Heidelberg Germany Natl Ctr Tumor Dis NCT Partnership DKFZ & Univ Med Ctr Heidelberg NCT Heidelberg Heidelberg Germany Heidelberg Univ Fac Math & Comp Sci Heidelberg Germany Karlsruhe Heidelberg HIDSS4Hlth Helmholtz Informat & Data Sci Sch Hlth Karlsruhe Germany German Canc Res Ctr Helmholtz Imaging Heidelberg Germany UBA Inst Ciencias Comp CONICET Buenos Aires DF Argentina German Canc Res Ctr Interact Machine Learning Grp Heidelberg Germany Heidelberg Univ Med Fac Heidelberg Germany

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Domain gaps are among the most relevant roadblocks in the clinical translation of machine learning (ML)-based solutions for medical image analysis. While current research focuses on new training paradigms and network architectures, little attention is given to the specific effect of prevalence shifts on an algorithm deployed in practice. Such discrepancies between class frequencies in the data used for a method's development/validation and that in its deployment environment(s) are of great importance, for example in the context of artificial intelligence (AI) democratization, as disease prevalences may vary widely across time and location. Our contribution is twofold. First, we empirically demonstrate the potentially severe consequences of missing prevalence handling by analyzing (i) the extent of miscalibration, (ii) the deviation of the decision threshold from the optimum, and (iii) the ability of validation metrics to reflect neural network performance on the deployment population as a function of the discrepancy between development and deployment prevalence. Second, we propose a workflow for prevalence-aware image classification that uses estimated deployment prevalences to adjust a trained classifier to a new environment, without requiring additional annotated deployment data. Comprehensive experiments based on a diverse set of 30 medical classification tasks showcase the benefit of the proposed workflow in generating better classifier decisions and more reliable performance estimates compared to current practice.

关键词： Prevalence shift medical image classification Generalization Domain Gap

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Bi-directional Encoding for Explicit Centerline Segmentation by Fully-Convolutional Networks 1

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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: (数字)9783031164408

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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SHISRCNet: Super-Resolution and Classification Network for Low-Resolution Breast Cancer Histopathology image 26th

SHISRCNet: Super-Resolution and Classification Network for L...

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

作者： Xie, Luyuan Li, Cong Wang, Zirui Zhang, Xin Chen, Boyan Shen, Qingni Wu, Zhonghai Peking Univ Sch Software & Microelect Beijing Peoples R China Peking Univ Natl Engn Res Ctr Software Engn Beijing 100871 Peoples R China Tencent Cloud Media Shenzhen Peoples R China

ISBN: (纸本)9783031439032;9783031439049

the rapid identification and accurate diagnosis of breast cancer, known as the killer of women, have become greatly significant for those patients. Numerous breast cancer histopathological image classification methods have been proposed. But they still suffer from two problems. (1) these methods can only hand high-resolution (HR) images. However, the low-resolution (LR) images are often collected by the digital slide scanner with limited hardware conditions. Compared with HR images, LR images often lose some key features like texture, which deeply affects the accuracy of diagnosis. (2) the existing methods have fixed receptive fields, so they can not extract and fuse multi-scale features well for images with different magnification factors. To fill these gaps, we present a Single Histopathological image Super-Resolution Classification network (SHISRCNet), which consists of two modules: Super-Resolution (SR) and Classification (CF) modules. SR module reconstructs LR images into SR ones. CF module extracts and fuses the multi-scale features of SR images for classification. In the training stage, we introduce HR images into the CF module to enhance SHIS-RCNet's performance. Finally, through the joint training of these two modules, super-resolution and classified of LR images are integrated into our model. the experimental results demonstrate that the effects of our method are close to the SOTA methods with taking HR images as inputs.

关键词： breast cancer histopathological image super-resolution classification joint training

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Detecting Unforeseen Data Properties with Diffusion Autoencoder Embeddings Using Spine MRI Data 9th

Detecting Unforeseen Data Properties with Diffusion Autoenco...

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

作者： Graf, Robert Hunecke, Florian Pohl, Soeren Atad, Matan Moeller, Hendrik Starck, Sophie Kroencke, thomas Bette, Stefanie Bamberg, Fabian Pischon, Tobias Niendorf, thoralf Schmidt, Carsten Paetzold, Johannes C. Rueckert, Daniel Kirschke, Jan S. Klinikum Rechts Der Isar TUM Sch Med & Hlth Dept Diagnost & Intervent Neuroradiol Munich Germany TUM Inst Fuer KI & Informat Med Klinikum Rechts Isar Munich Germany Imperial Coll London Dept Comp Biomed Image Anal Grp London England Univ Hosp Augsburg Diagnost & Intervent Radiol Augsburg Germany Univ Klinkum Freiburg Klin Fuer Diagnost & Intervent Radiol Freiburg Im Breisgau Germany Max Delbrueck Ctr Mol Med Helmholtz Assoc MDC Mol Epidemiol Res Grp Berlin Germany Max Delbruck Ctr Mol Med Helmholtz Assoc MDC Berlin Ultrahigh Field Facil Berlin Germany Univ Med Greifswald Greifswald Germany

ISBN: (纸本)9783031776090;9783031776106

Deep learning has made significant strides in medical imaging, leveraging the use of large datasets to improve diagnostics and prognostics. However, large datasets often come with inherent errors through subject selection and acquisition. In this paper, we investigate the use of Diffusion Autoencoder (DAE) embeddings for uncovering and understanding data characteristics and biases, including biases for protected variables like sex and data abnormalities indicative of unwanted protocol variations. We use sagittal T2-weighted magnetic resonance (MR) images of the neck, chest, and lumbar region from 11186 German National Cohort (NAKO) participants. We compare DAE embeddings with existing generative models like StyleGAN and Variational Autoencoder. Evaluations on a large-scale dataset consisting of sagittal T2-weighted MR images of three spine regions show that DAE embeddings effectively separate protected variables such as sex and age. Furthermore, we used t-SNE visualization to identify unwanted variations in imaging protocols, revealing differences in head positioning. Our embedding can identify samples where a sex predictor will have issues learning the correct sex. Our findings highlight the potential of using advanced embedding techniques like DAEs to detect data quality issues and biases in medical imaging datasets. Identifying such hidden relations can enhance the reliability and fairness of deep learning models in healthcare applications, ultimately improving patient care and outcomes.

关键词： Bias detection Data Quality Embeddings Large Cohorts

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Domain Generalization in Myocardial Pathology Segmentation with MixUp Augmentation 1st

Domain Generalization in Myocardial Pathology Segmentation ...

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1st MICCAI Challenge Comprehensive Analysis and computing of Real-World medical images, CARE 2024 Held in Conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Chen, Leyang Tu, Yaosheng Bai, Penggang Pan, Lin College of Physics and Information Engineering Fuzhou University Fuzhou China Department of Radiation Oncology Fujian Cancer Hospital and Fujian Medical University Cancer Hospital Fujian Fuzhou China

ISBN: (纸本)9783031870088

MyoPS (Myocardial Pathology Segmentation) is used for auxiliary diagnosis of myocardial infarction by accurately segmenting myocardial lesions (such as scars and edema). However, CMR images are complex, manual segmentation is time-consuming and relies on professional knowledge, and there are differences in imaging data from different centers, which increases the difficulty of segmentation. To this end, this study developed a domain generalization module that flexibly integrates LGE, T2-weighted, and Cine sequences to improve cross-center and multi-sequence adaptability and robustness. Our method combines the domain generalization module with the nnUNet segmentation network, and reduces the differences between different data distributions by utilizing the domain generalization module for data mixing enhancement, thereby enhancing the model’s generalization ability and improving segmentation performance. In tests conducted on the data set of the MyoPS++ Challenge, our network performed well in segmenting scars and edema. Compared with the native segmentation network, it has a greater performance improvement, which verifies its ability to handle multi-center, Effectiveness in multi-sequence CMR data. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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Enhance Multi-modal and Multi-center Whole Heart Segmentation Using Data Augmentation and Model Calibration 1st

Enhance Multi-modal and Multi-center Whole Heart Segmentati...

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作者： Tran, Charlie Li, Andy Espinoza, Aaron Kamal, Sayem Samuel, Anoushka Jiang, Charles Zhuang, Jian Shi, Yiyu Xu, Xiaowei University of Florida GainesvilleFL32611 United States University of Texas at Austin AustinTX78712 United States Texas State University San MarcosTX78666 United States Columbia University New York CityNY10027 United States California Polytechnic State University San Luis ObispoCA93410 United States University of Notre Dame Notre DameIN46556 United States Guangdong Provincial People’s Hospital Guangzhou510000 China

ISBN: (纸本)9783031870088

Accurate whole heart segmentation can enhance the modeling and analysis of cardiovascular disease treatment. However, real-world imaging analysis faces challenges in generalization due to data inhomogeneity, which arises from factors such as low-quality image acquisition, variations across multiple modalities, differences in scanner vendors, and cardiac motion. In this paper, to tackle the problem of data inhomogeneity, we use data augmentation and model calibration for whole heart segmentation enhancement. We first adopted the state-of-the-art MedNeXt transformer-based architecture as the baseline. then, we enhance the baseline in model robustness through an ensemble strategy with strong data augmentation and out-of-distribution model calibration techniques. Comprehensive experiments have been conducted on the dataset from the CARE2024 Task 5 Whole Heart Segmentation (WHS++) challenge, in which 7 target structures from CT and MRI images acquired from multiple centers are considered. Results show that data augmentation and model calibration can effectively improve the segmentation performance across various modalities and centers. We highlight that our team ranks first on the validation leaderboard with average dice scores of 0.9440 (CT) and 0.8956 (MRI). © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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Fast Non-Markovian Diffusion Model for Weakly Supervised Anomaly Detection in Brain MR images 26th

Fast Non-Markovian Diffusion Model for Weakly Supervised Ano...

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

作者： Li, Jinpeng Cao, Hanqun Wang, Jiaze Liu, Furui Dou, Qi Chen, Guangyong Heng, Pheng-Ann Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Chinese Univ Hong Kong Inst Med Intelligence & XR Hong Kong Peoples R China Zhejiang Lab Hangzhou Peoples R China

ISBN: (纸本)9783031439032;9783031439049

In medical image analysis, anomaly detection in weakly supervised settings has gained significant interest due to the high cost associated with expert-annotated pixel-wise labeling. Current methods primarily rely on auto-encoders and flow-based healthy image reconstruction to detect anomalies. However, these methods have limitations in terms of high-fidelity generation and suffer from complicated training processes and low-quality reconstructions. Recent studies have shown promising results with diffusion models in image generation. However, their practical value in medical scenarios is restricted due to their weak detail-retaining ability and low inference speed. To address these limitations, we propose a fast non-Markovian diffusion model (FNDM) with hybrid-condition guidance to detect high-precision anomalies in the brain MR images. A non-Markovian diffusion process is designed to enable the efficient transfer of anatomical information across diffusion steps. Additionally, we introduce new hybrid pixel-wise conditions as more substantial guidance on hidden states, which enables the model to concentrate more efficiently on the anomaly regions. Furthermore, to reduce computational burden during clinical applications, we have accelerated the encoding and sampling procedures in our FNDM using multi-step ODE solvers. As a result, our proposed FNDM method outperforms the previous state-of-the-art diffusion model, achieving a 9.56% and 19.98% improvement in Dice scores on the BRATS 2020 and ISLES datasets, respectively, while requiring only six times less computational cost.

关键词： Anomaly Detection Diffusion Probabilistic Model medical image Analysis

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Multi-scope Analysis Driven Hierarchical Graph Transformer for Whole Slide image Based Cancer Survival Prediction 26th

Multi-scope Analysis Driven Hierarchical Graph Transformer f...

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

作者： Hou, Wentai He, Yan Yao, Bingjian Yu, Lequan Yu, Rongshan Gao, Feng Wang, Liansheng Xiamen Univ Sch Informat Dept Informat & Commun Engn Xiamen Peoples R China Xiamen Univ Sch Informat Dept Comp Sci Xiamen Peoples R China Univ Hong Kong Dept Stat & Actuarial Sci Hong Kong Peoples R China Sun Yat Sen Univ Affiliated Hosp 6 Guangzhou Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Cancer survival prediction requires considering not only the biological morphology but also the contextual interactions of tumor and surrounding tissues. the major limitation of previous learning frameworks for whole slide image (WSI) based survival prediction is that the contextual interactions of pathological components (e.g., tumor, stroma, lymphocyte, etc.) lack sufficient representation and quantification. In this paper, we proposed a multi-scope analysis driven Hierarchical Graph Transformer (HGT) to overcome this limitation. Specifically, we first utilize a multi-scope analysis strategy, which leverages an in-slide superpixel and a cross-slide clustering, to mine the spatial and semantic priors of WSIs. Furthermore, based on the extracted spatial prior, a hierarchical graph convolutional network is proposed to progressively learn the topological features of the variant microenvironments ranging from patch-level to tissue-level. In addition, guided by the identified semantic prior, tissue-level features are further aggregated to represent the meaningful pathological components, whose contextual interactions are established and quantified by the designed Transformer-based prediction head. We evaluated the proposed framework on our collected Colorectal Cancer (CRC) cohort and two public cancer cohorts from the TCGA project, i.e., Liver Hepatocellular Carcinoma (LIHC) and Kidney Clear Cell Carcinoma (KIRC). Experimental results demonstrate that our proposed method yields superior performance and richer interpretability compared to the state-of-the-art approaches.

关键词： Whole slide image Survival prediction Contextual interaction Graph neural network Transformer

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