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

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TCEIP: Text Condition Embedded Regression Network for Dental Implant Position Prediction 26th

TCEIP: Text Condition Embedded Regression Network for Dental...

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

作者： Yang, Xinquan Xie, Jinheng Li, Xuguang Li, Xuechen Li, Xin Shen, Linlin Deng, Yongqiang Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Shenzhen Univ AI Res Ctr Med Image Anal & Diag Shenzhen Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen Peoples R China Shenzhen Univ Gen Hosp Dept Stomatol Shenzhen Peoples R China Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9783031439865;9783031439872

When deep neural network has been proposed to assist the dentist in designing the location of dental implant, most of them are targeting simple cases where only one missing tooth is available. As a result, literature works do not work well when there are multiple missing teeth and easily generate false predictions when the teeth are sparsely distributed. In this paper, we are trying to integrate a weak supervision text, the target region, to the implant position regression network, to address above issues. We propose a text condition embedded implant position regression network (TCEIP), to embed the text condition into the encoder-decoder framework for improvement of the regression performance. A cross-modal interaction that consists of cross-modal attention (CMA) and knowledge alignment module (KAM) is proposed to facilitate the interaction between features of images and texts. the CMA module performs a cross-attention between the image feature and the text condition, and the KAM mitigates the knowledge gap between the image feature and the image encoder of the CLIP. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TCEIP achieves superior performance than existing methods.

关键词： Dental Implant Deep Learning Text Guided Detection Cross-Modal Interaction

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SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints

SegHeD: Segmentation of Heterogeneous Data for Multiple Sc...

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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： Basaran, Berke Doga Zhang, Xinru Matthews, Paul M. Bai, Wenjia Department of Computing Imperial College London London United Kingdom Data Science Institute Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom School of Integrated Circuits and Electronics Beijing Institute of Technology CN Beijing China UK Dementia Research Institute Imperial College London London United Kingdom Rosalind Franklin Institute Didcot United Kingdom

ISBN: (纸本)9783031845246

Assessment of lesions and their longitudinal progression from brain magnetic resonance (MR) images plays a crucial role in diagnosing and monitoring multiple sclerosis (MS). Machine learning models have demonstrated a great potential for automated MS lesion segmentation. Training such models typically requires large-scale high-quality datasets that are consistently annotated. However, MS imaging datasets are often small, segregated across multiple sites, with different formats (cross-sectional or longitudinal), and diverse annotation styles. this poses a significant challenge to train a unified MS lesion segmentation model. To tackle this challenge, we present SegHeD, a novel multi-dataset multi-task segmentation model that can incorporate heterogeneous data as input and perform all-lesion, new-lesion, as well as vanishing-lesion segmentation. Furthermore, we account for domain knowledge about MS lesions, incorporating longitudinal, spatial, and volumetric constraints into the segmentation model. SegHeD is assessed on five MS datasets and achieves a high performance in all, new, and vanishing-lesion segmentation, outperforming several state-of-the-art methods in this field. © 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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SSL Based Encoder Pretraining for Segmenting a Heterogeneous Chronic Wound image Database with Few Annotations 4th

SSL Based Encoder Pretraining for Segmenting a Heterogeneo...

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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： Picaud, Guillaume Chaumont, Marc Subsol, Gérard Téot, Luc LIRMM équipe ICAR Univ. Montpellier CNRS Montpellier France Univ. Nîmes Place Gabriel Péri Nîmes France Cicat-Occitanie Montpellier France

ISBN: (纸本)9783031808708

Segmentation is crucial in medical imaging, but obtaining a sufficient quantity of annotated data is challenging, limiting the development of high-performing deep learning models. Self-supervised learning (SSL) strategies offer a promising solution to address this lack of annotation. One such strategy, Dinov2 for Distillation with NO labels, enabled the creation of the vast LVD-142M database and the training of encoders, whose weights are now freely accessible. However, clinical images may not be well represented in LVD-142M. thus, in the context of scarce annotated clinical data, we evaluate the benefits of a generic encoder pre-trained with DINO on LVD-142M versus a lighter one. We also explore the effect of SSL DINO pre-training strategy directly on the target dataset. We measure the impact of available label quantity on segmentation performances. Results show, in a context with few annotated images, specific and lightweight encoder can outperform generically pre-trained DINO one. Furthermore, DINO SSL pre-training on specific dataset is beneficial for small encoder. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Source-Free Domain Adaptive Fundus image Segmentation with Class-Balanced Mean Teacher 26th

Source-Free Domain Adaptive Fundus Image Segmentation with C...

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

作者： Tang, Longxiang Li, Kai He, Chunming Zhang, Yulun Li, Xiu Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China NEC Labs Amer Princeton NJ 08540 USA Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9783031439063;9783031439070

this paper studies source-free domain adaptive fundus image segmentation which aims to adapt a pretrained fundus segmentation model to a target domain using unlabeled images. this is a challenging task because it is highly risky to adapt a model only using unlabeled data. Most existing methods tackle this task mainly by designing techniques to carefully generate pseudo labels from the model's predictions and use the pseudo labels to train the model. While often obtaining positive adaption effects, these methods suffer from two major issues. First, they tend to be fairly unstable - incorrect pseudo labels abruptly emerged may cause a catastrophic impact on the model. Second, they fail to consider the severe class imbalance of fundus images where the foreground (e.g., cup) region is usually very small. this paper aims to address these two issues by proposing the Class-Balanced Mean Teacher (CBMT) model. CBMT addresses the unstable issue by proposing a weak-strong augmented mean teacher learning scheme where only the teacher model generates pseudo labels from weakly augmented images to train a student model that takes strongly augmented images as input. the teacher is updated as the moving average of the instantly trained student, which could be noisy. this prevents the teacher model from being abruptly impacted by incorrect pseudo-labels. For the class imbalance issue, CBMT proposes a novel loss calibration approach to highlight foreground classes according to global statistics. Experiments show that CBMT well addresses these two issues and outperforms existing methods on multiple benchmarks.

关键词： Source-free domain adaptation Fundus image Mean teacher

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Synthesizing 3D Axon Morphology: Springs are All We Need 15th

Synthesizing 3D Axon Morphology: Springs are All We Need

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15th international Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Cui, Ruiqi Bærentzen, J. Andreas Dyrby, Tim B. Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby2800 Denmark Danish Research Centre for Magnetic Resonance Centre for Functional and Diagnostic Imaging and Research Copenhagen University Hospital - Amager and Hvidovre Hvidovre2650 Denmark

ISBN: (纸本)9783031869198

the realism of digital phantoms for the white matter microstructure is highly valued. Realistic synthesis provides reliable input to generate synthetic diffusion MRI signals for evaluating biophysical models or training machine learning models of microstructure features, such as axon diameter, shapes, and cellular structures. Inspired by the popular spring-mass systems used in physical simulation, we propose a novel and flexible method for synthesizing axon morphology and its dynamics with physical constraints. Specifically, starting with an initial axon configuration, our method constructs a spring-mass system based on specific sampling rules inspired by the real 3D axons and cell morphology observed in X-ray synchrotron imaging. By minimizing the spring potential energy, our method optimizes the positions of sampled mass points, thereby deforming the axon morphology from its physical surroundings. After the optimization, a triangle mesh of the axon surfaces is obtained and can be used as input for Monte Carlo diffusion MRI simulations. Experimental results demonstrate that our approach successfully mimics a range of axon morphologies and the dynamic environment. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image coding

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Multimodality Frequency Feature Customized Learning for Pediatric Ventricular Septal Defects Identification

Multimodality Frequency Feature Customized Learning for Pedi...

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作者： Jin, Feifei Zhao, Cheng Yang, Peng Xiang, Zhuo Chen, Xunyi Zhang, Yu Fan, Shumin Zhou, Luyao Chen, Weiling Wang, Tianfu Lei, Baiying School of Biomedical Engineering Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging Shenzhen University National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Shenzhen China Ultrasound Department Shenzhen Children Hospital Shenzhen China

ISBN: (纸本)9783031845246

Ventricular septal defects (VSD) can be more effectively identified by combining anatomical structural features from 2D grayscale images and blood flow information from Doppler images. Most current algorithms only perform the same operation on features and do not take into account that different frequency features focus on the expression of different information. Starting from the perspective of frequency and multi-modality, this paper designs a method called multimodality frequency feature customized learning (MFCL) for the identification of VSD. Specifically, this paper first constructs a frequency decomposition module (FD) based on the Fourier transform to extract high-frequency and low-frequency features corresponding to different modes. Secondly, this paper designs a cross-pooling fusion Transformer (CPFT) module tailored for multimodality low-frequency features, which can achieve the fusion of multi-modality global information while reducing computational costs. this paper designs a cross-convolution fusion (CCF) module tailored for multi-modality high-frequency features to achieve the fusion of anatomical structure detail features of 2D grayscale images and blood flow information of color Doppler images. Experimental results show that the proposed algorithm is superior to the comparison methods for VSD identification. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image coding

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Beyond MobileNet: An Improved MobileNet for Retinal Diseases 1

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Myopic Maculopathy Analysis Challenge, MMAC 2023, which was a part of the 26th international conference on medical image computing and computer assisted intervention, MICCAI 2023

作者： Zhu, Wenhui Qiu, Peijie Chen, Xiwen Li, Huayu Wang, Hao Lepore, Natasha Dumitrascu, Oana M. Wang, Yalin School of Computing and Augmented Intelligence Arizona State University TempeAZ United States McKeley School of Engineering Washington University in St. Louis St. LouisMO United States School of Computing Clemson University ClemsonSC United States Department of Electrical and Computer Engineering The University of Arizona TucsonAZ United States Department of Neurology Mayo Clinic PhoenixAZ United States CIBORG Lab Department of Radiology Children’s Hospital Los Angeles Los AngelesCA United States

ISBN: (数字)9783031548574

ISBN: (纸本)9783031548567

Myopic Maculopathy (MM) is the leading cause of severe vision loss or blindness. Deep learning-based automated tools are indispensable in assisting clinicians in diagnosing and monitoring RD in modern medicine. Recently, an increasing number of works in this field have taken advantage of Vision Transformer to achieve state-of-the-art performance with more parameters and higher model complexity compared to Convolutional Neural Networks (CNNs). Such sophisticated model designs, however, are prone to be overfitting and hinder their advantages in specific tasks in medical image analysis. In this work, we argue that a well-calibrated CNN model may mitigate these problems. To this end, we empirically investigated the macro and micro designs of a CNN and its training strategies by starting with a standard MobileNet. Based on the investigation, we proposed a lightweight MobileNet training framework equipped with a series of optimal parameters and modules based on retinal images. As a result of performance, our model secured third place in the MICCAI MMAC 2023 Challenge - Classification of Myopic Maculopathy. Our software package is available at https://***/Retinal-Research/NN-MOBILENET. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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Revolutionizing Brain Tumor Diagnosis: Harnessing Convolutional Neural Networks for Enhanced Prediction and Classification 5

Revolutionizing Brain Tumor Diagnosis: Harnessing Convolutio...

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5th IEEE international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Singh, Aditya Kumar Mishra, Aman Galgotias University School of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

Diagnosing brain tumors correctly and on time is the only way of effectively planning suitable treatment. Advances made recently in the field of medical imaging and artificial intelligence are leading to better methods of diagnosis. this paper is going to explore how Convolutional Neural Networks (CNNs) can be used to predict and classify tumors in the brain. the challenges of diagnosing brain tumors, the importance of CNNs in medical image processing, a detailed survey about recent studies and techniques used in this area would also be covered. Moreover, we propose an innovative CNN architecture designed for brain tumor detection by using modern developments in deep *** said, a two stage analysis methodology was followed with a total focus towards prevalent challenges within image Restoration and image Enhancement. As such, this study introduces new techniques that address these issues *** study proposes an approach utilizing the power of Convolutional Neural Networks (CNNs). the novel CNN classification technique is applied, leveraging the Python and TensorFlow *** findings underscore the potential of this approach in revolutionizing brain tumor detection. By providing detailed insights into the strengths and limitations of the proposed model, this research contributes significantly to the *** paper concludes by emphasizing the transformative impact of this research, opening avenues for further exploration and innovation in this critical domain. © 2024 IEEE.

关键词： Brain Tumor Convolution Neural Network deep learning technique hyperparameters medical image optimization segmentation

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Privacy-Preserving Collaborative AI for Distributed Deep Learning with Cross-sectional Data 5

Privacy-Preserving Collaborative AI for Distributed Deep Lea...

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5th international conference on Bio-engineering for Smart Technologies, BioSMART 2023

作者： Iqbal, Saeed Qureshi, Adnan N. Aurangzeb, Khursheed Javeed, Khalid University of Central Punjab Faculty of Information Technology & Computer Science Department of Computer Science Lahore Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh Saudi Arabia University of Sharjah College of Computing and Informatics Department of Computer Engineering Sharjah United Arab Emirates

ISBN: (纸本)9798350338492

the article discusses the challenges of using deep learning in healthcare due to the lack of extensive medical datasets and concerns about confidentiality and privacy. the article then focuses on the analysis of skin lesion images, which are complex and difficult to classify. the article presents a unique CNN model (skin-net) using a progressively private federated learning architecture to potentially address these issues. Recent advancements in medical image analysis using convolutional neural networks and federated learning techniques have shown promising results (96.19 accuracy, 96.37 sensitivity and 96.53 specificity) in accurately classifying skin diseases while maintaining data privacy. Using these methods offers a potentially effective way to examine human skin while improving the model's generalization performance through image augmentation. © 2023 IEEE.

关键词： Deep learning

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