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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461-470 订阅

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Identification of Different Medicinal Plants Using Machine Learning and image Processing 5th

Identification of Different Medicinal Plants Using Machine L...

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5th EAI international conference on Cognitive computing and Cyber Physical Systems, IC4S 2024

作者： Pokkuluri, Kiran Sree Phaneendra Varma, Ch Gurujukota, Ramesh Babu Raja Rao, P.B.V. Usha Devi N, S.S.S.N. Prasad, M. Pamarthi, Nagaraju Shalem Raju, P.J.R. Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Computer Science and Engineering University College of Engineering JNTU Kakinada Kakinada India

ISBN: (纸本)9783031770746

Medicinal plants have been an essential source of remedies and treatments for various ailments throughout human history. this paper presents an innovative approach that combines machine learning and image processing to identify various medicinal plants and extract information about their traditional and modern uses. this method offers efficiency and dependability by automating the identification procedure, which lessens the reliance on human expertise and may hasten the discovery and application of medicinal plants for therapeutic purposes. In this work identification of the medical plats is done thought the shape of the plant leave, text features using digital image processing techniques, color using Convolutional Neural Works (CNN). We have achieved an accuracy of 98.6% in identifying the medical plants and we have compared our work with the existing literature with various parameters like precision, accuracy and f1 score. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Adversarial machine learning

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Joint Modeling of image and Label Statistics for Enhancing Model Generalizability of medical image Segmentation 25th

Joint Modeling of Image and Label Statistics for Enhancing M...

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

作者： Gao, Shangqi Zhou, Hangqi Gao, Yibo Zhuang, Xiahai Fudan Univ Sch Data Sci Shanghai 200433 Peoples R China

ISBN: (纸本)9783031164439;9783031164422

Although supervised deep-learning has achieved promising performance in medical image segmentation, many methods cannot generalize well on unseen data, limiting their real-world applicability. To address this problem, we propose a deep learning-based Bayesian framework, which jointly models image and label statistics, utilizing the domain-irrelevant contour of a medical image for segmentation. Specifically, we first decompose an image into components of contour and basis. then, we model the expected label as a variable only related to the contour. Finally, we develop a variational Bayesian framework to infer the posterior distributions of these variables, including the contour, the basis, and the label. the framework is implemented with neural networks, thus is referred to as deep Bayesian segmentation. Results on the task of cross-sequence cardiac MRI segmentation show that our method set a new state of the art for model generalizability. Particularly, the BayeSeg model trained with LGE MRI generalized well on T2 images and outperformed other models with great margins, i.e., over 0.47 in terms of average Dice. Our code is available at https: //***/***.

关键词： Bayesian segmentation image decomposition Model generalizability Deep learning

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Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

Longitudinal Segmentation of MS Lesions via Temporal Diffe...

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Workshop on Longitudinal Disease Tracking and Modeling with medical images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Rokuss, Maximilian R. Kirchhoff, Yannick Roy, Saikat Kovacs, Balint Ulrich, Constantin Wald, Tassilo Zenk, Maximilian Denner, Stefan Isensee, Fabian Vollmuth, Philipp Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Helmholtz Imaging German Cancer Research Center Heidelberg Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Neuroimaging Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031845246

Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level F1 score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at: ***/MIC-DKFZ/Longitudinal-Difference-Weighting. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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Cross-View Deformable Transformer for Non-displaced Hip Fracture Classification from Frontal-Lateral X-Ray Pair 26th

Cross-View Deformable Transformer for Non-displaced Hip Frac...

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

作者： Zhu, Zhonghang Chen, Qichang Yu, Lequan Wang, Lianxin Zhang, Defu Magnier, Baptiste Wang, Liansheng Xiamen Univ Sch Informat Dept Comp Sci Xiamen Peoples R China Univ Hong Kong Dept Stat & Actuarial Sci Hong Kong Peoples R China Xiamen Univ Affiliated Hosp 1 Dept Orthoped Xiamen Peoples R China Univ Montpellier IMT Mines Ales Euromov Digital Hlth Motion Ales France

ISBN: (纸本)9783031439865;9783031439872

Hip fractures are a common cause of morbidity and mortality and are usually diagnosed from the X-ray images in clinical routine. Deep learning has achieved promising progress for automatic hip fracture detection. However, for fractures where displacement appears not obvious (i.e., non-displaced fracture), the single-view X-ray image can only provide limited diagnostic information and integrating features from cross-view X-ray images (i.e., Frontal/Lateral-view) is needed for an accurate diagnosis. Nevertheless, it remains a technically challenging task to find reliable and discriminative cross-view representations for automatic diagnosis. First, it is difficult to locate discriminative task-related features in each X-ray view due to the weak supervision of image-level classification labels. Second, it is hard to extract reliable complementary information between different X-ray views as there is a displacement between them. To address the above challenges, this paper presents a novel cross-view deformable transformer framework to model relations of critical representations between different views for non-displaced hip fracture identification. Specifically, we adopt a deformable self-attention module to localize discriminative task-related features for each X-ray view only with the image-level label. Moreover, the located discriminative features are further adopted to explore correlated representations across views by taking advantage of the query of the dominated view as guidance. Furthermore, we build a dataset including 768 hip cases, in which each case has paired hip X-ray images (Frontal/Lateral-view), to evaluate our framework for the non-displaced fracture and normal hip classification task.

关键词： Hip fracture diagnosis X-ray image Deformable transformer Cross-view correspondence

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Data-Driven Multi-modal Partial medical image Preregistration by Template Space Patch Mapping 25th

Data-Driven Multi-modal Partial Medical Image Preregistratio...

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

作者： Xia, Ding Yang, Xi van Kaick, Oliver Kin, Taichi Igarashi, Takeo Univ Tokyo 7 Chome 3-1 Hong Bunkyo City Tokyo 1138654 Japan Jilin Univ 2699 Qianjin St Changchun 130012 Jilin Peoples R China Carleton Univ 1125 Colonel Ottawa ON K1S 5B6 Canada

ISBN: (纸本)9783031164460;9783031164453

image registration is an essential part of medical image Analysis. Traditional local search methods (e.g., Mean Square Errors (MSE) and Normalized Mutual Information (NMI)) achieve accurate registration but require good initialization. However, finding a good initialization is difficult in partial image matching. Recent deep learning methods such as images-to-transformation directly solve the registration problem but need images of mostly same sizes and already roughly aligned. this work presents a learning-based method to provide good initialization for partial image registration. A light and efficient network learns the mapping from a small patch of an image to a position in the template space for each modality. After computing such mapping for a set of patches, we compute a rigid transformation matrix that maps the patches to the corresponding target positions. We tested our method to register a 3DRA image of a partial brain to a CT image of a whole brain. the result shows that MSE registration with our initialization significantly outperformed baselines including naive initialization and recent deep learning methods without template. You can access our source code in https://***/ApisXia/PartialMedPreregistration.

关键词： Multi-modal Partial image Preregistration Patch mapping

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Y-Net: A Spatiospectral Dual-Encoder Network for medical image Segmentation 25th

Y-Net: A Spatiospectral Dual-Encoder Network for Medical Ima...

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

作者： Farshad, Azade Yeganeh, Yousef Gehlbach, Peter Navab, Nassir Tech Univ Munich Munich Germany Johns Hopkins Univ Baltimore MD USA

ISBN: (纸本)9783031164347;9783031164330

Automated segmentation of retinal optical coherence tomography (OCT) images has become an important recent direction in machine learning for medical applications. We hypothesize that the anatomic structure of layers and their high-frequency variation in OCT images make retinal OCT a fitting choice for extracting spectral domain features and combining them with spatial domain features. In this work, we present YNet , an architecture that combines the frequency domain features with the image domain to improve the segmentation performance of OCT images. the results of this work demonstrate that the introduction of two branches, one for spectral and one for spatial domain features, brings very significant improvement in fluid segmentation performance and allows outperformance as compared to the well-known U-Net model. Our improvement was 13% on the fluid segmentation dice score and 1.9% on the average dice score. Finally, removing selected frequency ranges in the spectral domain demonstrates the impact of these features on the fluid segmentation outperformance. Code: ***/azadef/ynet

关键词： OCT segmentation Frequency domain in OCT U-Net

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Multi-task Framework for Myocardial Pathological Segmentation on Multi-sequence Cardiac Magnetic Resonance 1st

Multi-task Framework for Myocardial Pathological Segmentati...

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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： Wu, Yilin Yang, Xiao Liu, Yongli Zheng, Jiahao Yang, Mingjing College of Physics and Information Engineering Fuzhou University Fuzhou350108 China

ISBN: (纸本)9783031870088

Accurate segmentation of myocardial pathology, including scar and edema, from magnetic resonance images (MRI) is of significant clinical importance for diagnosing, planning treatment, and assessing prognosis in cardiovascular diseases. Multi-sequence cardiac magnetic resonance images provide additional information for pathological segmentation, while ventricular blood pool regions offer positional anatomical priors for identifying scar and edema. therefore, we propose a multi-task framework based on SimAM-UNet, where SimAM generates feature weights to better focus on modality-specific features for the segmentation of myocardial pathology using multi-sequence cardiac magnetic resonance images. the first task involves segmenting and extracting features of the myocardium and blood pool, which are relatively easy to obtain. the second task focuses on segmenting scars and edema by integrating positional feature constraints derived from the myocardium and blood pool. To address the multi-center data problem encountered in real-world data, we incorporate a data augmentation pipeline that simulates the generation of data samples from different centers, enhancing the domain generalization of our method. Our proposed method was evaluated on the MyoPS++ track of the CARE2024 Challenge, and the experimental results demonstrated the effectiveness of our multi-task framework for myocardial pathological segmentation. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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A Sense of Direction in Biomedical Neural Networks 25th

A Sense of Direction in Biomedical Neural Networks

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

作者： Liu, Zewen Cootes, Timothy F. Univ Manchester Div Informat Imaging & Data Sci Stopford Bldg Manchester M13 9PT England

ISBN: (纸本)9783031164439;9783031164422

We describe an approach to making a model be aware of not only intensity but also properties such as feature direction and scale. Such properties can be important when analysing images containing curvilinear structures such as vessels or fibres. We propose the General Multi-Angle Scale Convolution (G-MASC), whose kernels are arbitrarily rotatable and also fully differentiable. the model manages its directional detectors in sets, and supervises a set's rotation symmetricity with a novel rotation penalty called PoRE. the algorithm works on pyramid representations to enable scale search. Direction and scale can be extracted from the output maps, encoded and analysed separately. Tests were conducted on three public datasets, MoNuSeg, DRIVE, and CHASE-DB1. Good performance is observed while the model requires 1% or fewer parameters compared to other approaches such as U-Net.

关键词： Steerable filter Intepretability image Segmentation

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Federated medical image Analysis with Virtual Sample Synthesis 1

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

作者： Zhu, Wei Luo, Jiebo Univ Rochester Rochester NY USA

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Hospitals and research institutions may not be willing to share their collected medical data due to privacy concerns, transmission cost, and the intrinsic value of the data. Federated medical image analysis is thus explored to obtain a global model without access to the images distributed on isolated clients. However, in real-world applications, the local data from each client are likely non-i.i.d distributed because of the variations in geographic factors, patient demographics, data collection process, and so on. Such heterogeneity in data poses severe challenges to the performance of federated learning. In this paper, we introduce federated medical image analysis with virtual sample synthesis (FedVSS). Our method can improve the generalization ability by adversarially synthesizing virtual training samples with the local models and also learn to align the local models by synthesizing high-confidence samples with regard to the global model. All synthesized data will be further utilized in local model updating. We conduct comprehensive experiments on five medical image datasets retrieved from MedMNIST and Camelyon17, and the experimental results validate the effectiveness of our method. Our code is available at Link.

关键词： medical image analysis Federated learning Adversarial learning

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Diabetic Foot Ulcer Grand Challenge 2024: Overview and Baseline Methods 4th

Diabetic Foot Ulcer Grand Challenge 2024: Overview and Base...

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4th Diabetic Foot Ulcers Grand Challenge, DFUC 2024 held in Conjunction 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Brüngel, Raphael Kendrick, Connah Cassidy, Bill Bracke, Benjamin Friedrich, Christoph M. Reeves, Neil D. Pappachan, Joseph M. Yap, Moi Hoon Dortmund44227 Germany University Hospital Essen Essen45130 Germany University Hospital Essen Essen45131 Germany Department of Computing and Mathematics Manchester Metropolitan University ManchesterM1 5GD United Kingdom Lancaster University LancasterLA1 4YW United Kingdom Lancashire Teaching Hospitals NHS Foundation Trust PrestonPR2 9HT United Kingdom

ISBN: (纸本)9783031808708

In its 4th edition, the Diabetic Foot Ulcer Challenge (DFUC) 2024 focuses on the task of Diabetic Foot Ulcer (DFU) segmentation by involving aspects of self-supervised learning as contrast to the DFUC 2022. this paper provides information on the challenge background, outlines details on an synthetic extension of the DFU dataset, elaborates rules for participation, and reports performance of two vastly different baseline methods that highlight the potential diversity of welcome approaches. It further provides a condensed summary on final challenge results and methods employed by participating teams. A discussion puts the different approaches and outcomes into perspective, considering their creativity and applicability on a larger scale. Eventually, a retrospective on the DFUC 2024 finds the given task to be considerably challenging, inspiring diverse contributions. Even though accurate DFU segmentation currently seems out of reach when involving a higher level of self-supervision, respective contributions demonstrate promising kick-off strategies for scenarios in which unlabelled data needs to be harnessed. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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