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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241-250 订阅

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DULDA: Dual-Domain Unsupervised Learned Descent Algorithm for PET image Reconstruction 26th

DULDA: Dual-Domain Unsupervised Learned Descent Algorithm fo...

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

作者： Hu, Rui Chen, Yunmei Kim, Kyungsang Rockenbach, Marcio Aloisio Bezerra Cavalcanti Li, Quanzheng Liu, Huafeng Zhejiang Univ Dept Opt Engn State Key Lab Modern Opt Instrumentat Hangzhou 310027 Peoples R China Univ Florida Dept Math Gainesville FL 32611 USA Harvard Med Sch Massachusetts Gen Hosp Ctr Adv Med Comp & Anal Boston MA 02114 USA Massachusetts Gen Brigham Data Sci Off Boston MA 02116 USA

ISBN: (纸本)9783031439988;9783031439995

Deep learning based PET image reconstruction methods have achieved promising results recently. However, most of these methods follow a supervised learning paradigm, which rely heavily on the availability of high-quality training labels. In particular, the long scanning time required and high radiation exposure associated with PET scans make obtaining these labels impractical. In this paper, we propose a dual-domain unsupervised PET image reconstruction method based on learned descent algorithm, which reconstructs high-quality PET images from sinograms without the need for image labels. Specifically, we unroll the proximal gradient method with a learnable l(2,1) norm for PET image reconstruction problem. the training is unsupervised, using measurement domain loss based on deep image prior as well as image domain loss based on rotation equivariance property. the experimental results demonstrate the superior performance of proposed method compared with maximum-likelihood expectation-maximization (MLEM), total-variation regularized EM (EM-TV) and deep image prior based method (DIP).

关键词： image reconstruction Positron emission tomography (PET) Unsupervised learning Model based deep learning Dual-domain

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Mitosis Detection from Partial Annotation by Dataset Generation via Frame-Order Flipping 26th

Mitosis Detection from Partial Annotation by Dataset Generat...

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

作者： Nishimura, Kazuya Katanaya, Ami Chuma, Shinichiro Bise, Ryoma Kyushu Univ Fukuoka Japan Kyoto Univ Kyoto Japan

ISBN: (纸本)9783031439926;9783031439933

Detection of mitosis events plays an important role in biomedical research. Deep-learning-based mitosis detection methods have achieved outstanding performance with a certain amount of labeled data. However, these methods require annotations for each imaging condition. Collecting labeled data involves time-consuming human labor. In this paper, we propose a mitosis detection method that can be trained with partially annotated sequences. the base idea is to generate a fully labeled dataset from the partial labels and train a mitosis detection model with the generated dataset. First, we generate an image pair not containing mitosis events by frame-order flipping. then, we paste mitosis events to the image pair by alpha-blending pasting and generate a fully labeled dataset. We demonstrate the performance of our method on four datasets, and we confirm that our method outperforms other comparisons which use partially labeled sequences. Code is available at https://***/naivete5656/MDPAFOF.

关键词： Partial annotation Mitosis detection Fluorescent image

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Certification of Deep Learning Models for medical image Segmentation 26th

Certification of Deep Learning Models for Medical Image Segm...

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

作者： Laousy, Othmane Araujo, Alexandre Chassagnon, Guillaume Paragios, Nikos Revel, Marie-Pierre Vakalopoulou, Maria Univ Paris Saclay Cent Supelec MICS Gif Sur Yvette France Paris Cite Univ Hop Cochin AP HP Paris France Inria Saclay Gif Sur Yvette France NYU New York NY USA Therapanacea Paris France

ISBN: (纸本)9783031439001;9783031439018

In medical imaging, segmentation models have known a significant improvement in the past decade and are now used daily in clinical practice. However, similar to classification models, segmentation models are affected by adversarial attacks. In a safety-critical field like healthcare, certifying model predictions is of the utmost importance. Randomized smoothing has been introduced lately and provides a framework to certify models and obtain theoretical guarantees. In this paper, we present for the first time a certified segmentation baseline for medical imaging based on randomized smoothing and diffusion models. Our results show that leveraging the power of denoising diffusion probabilistic models helps us overcome the limits of randomized smoothing. We conduct extensive experiments on five public datasets of chest X-rays, skin lesions, and colonoscopies, and empirically show that we are able to maintain high certified Dice scores even for highly perturbed images. Our work represents the first attempt to certify medical image segmentation models, and we aspire for it to set a foundation for future benchmarks in this crucial and largely uncharted area.

关键词： Certified Robustness Randomized Smoothing Denoising Diffusion Models Segmentation

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ACC-UNet: A Completely Convolutional UNet Model for the 2020s 1

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

作者： Ibtehaz, Nabil Kihara, Daisuke Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA Purdue Univ Dept Biol Sci W Lafayette IN 47907 USA

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

this decade is marked by the introduction of Vision Transformer, a radical paradigm shift in broad computer vision. A similar trend is followed in medical imaging, UNet, one of the most influential architectures, has been redesigned with transformers. Recently, the efficacy of convolutional models in vision is being reinvestigated by seminal works such as ConvNext, which elevates a ResNet to Swin Transformer level. Deriving inspiration from this, we aim to improve a purely convolutional UNet model so that it can be on par with the transformer-based models, e.g., Swin-Unet or UCTransNet. We examined several advantages of the transformer-based UNet models, primarily long-range dependencies and cross-level skip connections. We attempted to emulate them through convolution operations and thus propose, ACC-UNet, a completely convolutional UNet model that brings the best of both worlds, the inherent inductive biases of convnets with the design decisions of transformers. ACC-UNet was evaluated on 5 different medical image segmentation benchmarks and consistently outperformed convnets, transformers, and their hybrids. Notably, ACC-UNet outperforms state-of-the-art models Swin-Unet and UCTransNet by 2.64 +/- 2.54% and 0.45 +/- 1.61% in terms of dice score, respectively, while using a fraction of their parameters (59.26% and 24.24%). Our codes are available at https://***/ kiharalab/ACC- UNet.

关键词： UNet image segmentation fully convolutional network

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Gaze-assisted Autism Spectrum Disorder Identification: A Fusion of Machine Learning and Deep Learning Approaches for Preemptive Identification 5

Gaze-Assisted Autism Spectrum Disorder Identification: A Fus...

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5th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2024

作者： Simon, Judy Kapileswar, N. Datchinamoorthi, M. Muthukumar, S. Keerthana Devi, G. SRM Institute of Science and Technology Dept of Electronics and Communication Engineering Chennai India SRM Institute of Science and Technology Dept of Electronics and Computer Engineering Chennai India

ISBN: (纸本)9798350395235

this research proposes an all-encompassing method for diagnosing Autism Spectrum Disorder (ASD) by leveraging eye-tracking data and advanced machine learning techniques. In this study, gaze-tracking data was collected from individuals diagnosed with Autism Spectrum Disorder and those not exhibiting ASD characteristics as they participated in various tasks and stimuli. Principal Component Analysis was employed to streamline the eye-tracking data, retaining crucial information while enhancing computational efficiency. these refined features were then integrated into Convolutional Neural Networks as the primary modelling approach. CNNs excel in recognizing relevant spatial and temporal patterns within the visual data, leading to intricate feature extraction and classification. Additionally, we explored the efficacy of transfer learning by enhancing pre-trained CNN models with our eye-tracking dataset. this approach enables us to leverage knowledge from large-scale visual tasks, potentially enhancing the model's performance in ASD classification. the research findings highlight the success of this multifaceted approach in accurately categorizing individuals with ASD. the synergy of ML, CNN, and transfer learning models significantly enhances the accuracy of ASD diagnosis, offering a promising avenue for early identification and intervention. Consequently, this research has the potential to aid in the creation of accurate noninvasive instruments for diagnosing ASD, thereby accelerating timely assistance and treatment for individuals impacted by the condition. © 2024 IEEE.

关键词： image classification

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Tracking Lesion Evolution Using a Boundary Enhanced Approach for MS Change Segmentation (BEAMS)

Tracking Lesion Evolution Using a Boundary Enhanced Approac...

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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： Mathur, Prateek Kelly, Brendan S. Killeen, Ronan P Lawlor, Aonghus School of Computer Science University College Dublin Dublin Ireland School of Medicine University College Dublin Dublin Ireland Insight SFI Research Centre for Data Analytics Dublin Ireland St. Vincent’s University Hospital Dublin Ireland

ISBN: (纸本)9783031845246

Multiple sclerosis (MS) is a chronic disease of the Central Nervous System (CNS) primarily characterised on Magnetic Resonance Imaging (MRI) by hyper-intense lesions. Accurate and timely identification of new lesions or growth in existing lesions has a tremendous impact on treatment planning and patient care. Early detection can lead to prompt intervention possibly slowing the disease progression. However, this task is challenging, costly and often prone to observer bias. Our approach consists of three key components (1) redefining the lesion segmentation problem as a change detection challenge focusing only on new and enhancing lesions;(2) adding an additional boundary detection task to an existing Convolutional Neural Network (CNN) to provide detailed context regarding the shape, size, and evolution of enhancing lesions;(3) evaluating the Fβ loss objective as an alternative to traditional segmentation loss objectives, aiming to optimise the model’s sensitivity and specificity. Furthermore, we employ Gradient-weighted Class Activation Mapping (GradCAM) to visualise the network’s attention, aiding in the interpretation and validation of our approach. By providing an objective evaluation, our method aims to streamline clinical decision-making processes for improved patient outcomes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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HartleyMHA: Self-attention in Frequency Domain for Resolution-Robust and Parameter-Efficient 3D image Segmentation 26th

HartleyMHA: Self-attention in Frequency Domain for Resolutio...

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

作者： Wong, Ken C. L. Wang, Hongzhi Syeda-Mahmood, Tanveer IBM Res Almaden Res Ctr San Jose CA 95120 USA

ISBN: (纸本)9783031439001;9783031439018

With the introduction of Transformers, different attention-based models have been proposed for image segmentation with promising results. Although self-attention allows capturing of long-range dependencies, it suffers from a quadratic complexity in the image size especially in 3D. To avoid the out-of-memory error during training, input size reduction is usually required for 3D segmentation, but the accuracy can be suboptimal when the trained models are applied on the original image size. To address this limitation, inspired by the Fourier neural operator (FNO), we introduce the HartleyMHA model which is robust to training image resolution with efficient self-attention. FNO is a deep learning framework for learning mappings between functions in partial differential equations, which has the appealing properties of zero-shot super-resolution and global receptive field. We modify the FNO by using the Hartley transform with shared parameters to reduce the model size by orders of magnitude, and this allows us to further apply self-attention in the frequency domain for more expressive high-order feature combination with improved efficiency. When tested on the BraTS'19 dataset, it achieved superior robustness to training image resolution than other tested models with less than 1% of their model parameters.

关键词： image segmentation Transformer Fourier neural operator Hartley transform Resolution-robust

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DHC: Dual-Debiased Heterogeneous Co-training Framework for Class-Imbalanced Semi-supervised medical image Segmentation 1

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

作者： Wang, Haonan Li, Xiaomeng Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

the volume-wise labeling of 3D medical images is expertise-demanded and time-consuming;hence semi-supervised learning (SSL) is highly desirable for training with limited labeled data. Imbalanced class distribution is a severe problem that bottlenecks the real-world application of these methods but was not addressed much. Aiming to solve this issue, we present a novel Dual-debiased Heterogeneous Cotraining (DHC) framework for semi-supervised 3D medical image segmentation. Specifically, we propose two loss weighting strategies, namely Distribution-aware Debiased Weighting (DistDW) and Difficulty-aware Debiased Weighting (DiffDW), which leverage the pseudo labels dynamically to guide the model to solve data and learning biases. the framework improves significantly by co-training these two diverse and accurate sub-models. We also introduce more representative benchmarks for classim-balanced semi-supervised medical image segmentation, which can fully demonstrate the efficacy of the class-imbalance designs. Experiments show that our proposed framework brings significant improvements by using pseudo labels for debiasing and alleviating the class imbalance problem. More importantly, our method outperforms the state-of-theart SSL methods, demonstrating the potential of our framework for the more challenging SSL setting. Code and models are available at: https://***/xmed-lab/DHC.

关键词： Semi-supervised learning Class imbalance 3D medical image segmentation CT image

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MetaLR: Meta-tuning of Learning Rates for Transfer Learning in medical Imaging 26th

MetaLR: Meta-tuning of Learning Rates for Transfer Learning ...

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

作者： Chen, Yixiong Liu, Li Li, Jingxian Jiang, Hua Ding, Chris Zhou, Zongwei Chinese Univ Hong Kong Shenzhen Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China Fudan Univ Software Sch Shanghai Peoples R China Johns Hopkins Univ Baltimore MD USA

ISBN: (纸本)9783031439063;9783031439070

In medical image analysis, transfer learning is a powerful method for deep neural networks (DNNs) to generalize on limited medical data. Prior efforts have focused on developing pre-training algorithms on domains such as lung ultrasound, chest X-ray, and liver CT to bridge domain gaps. However, we find that model fine-tuning also plays a crucial role in adapting medical knowledge to target tasks. the common fine-tuning method is manually picking transferable layers (e.g., the last few layers) to update, which is labor-expensive. In this work, we propose a meta-learning-based learning rate (LR) tuner, named MetaLR, to make different layers automatically co-adapt to downstream tasks based on their transferabilities across domains. MetaLR learns LRs for different layers in an online manner, preventing highly transferable layers from forgetting their medical representation abilities and driving less transferable layers to adapt actively to new domains. Extensive experiments on various medical applications show that MetaLR outperforms previous state-of-the-art (SOTA) fine-tuning strategies. Codes are released.

关键词： medical image analysis Meta-learning Transfer learning

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SAMConvex: Fast Discrete Optimization for CT Registration Using Self-supervised Anatomical Embedding and Correlation Pyramid 26th

SAMConvex: Fast Discrete Optimization for CT Registration Us...

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

作者： Li, Zi Tian, Lin Mok, Tony C. W. Bai, Xiaoyu Wang, Puyang Ge, Jia Zhou, Jingren Lu, Le Ye, Xianghua Yan, Ke Jin, Dakai DAMO Acad Alibaba Grp Hangzhou Peoples R China Univ N Carolina Chapel Hill NC 27515 USA Zhejiang Univ Coll Med Affiliated Hosp 1 Hangzhou Peoples R China Hupan Lab Hangzhou 310023 Peoples R China

ISBN: (纸本)9783031439988;9783031439995

Estimating displacement vector field via a cost volume computed in the feature space has shown great success in image registration, but it suffers excessive computation burdens. Moreover, existing feature descriptors only extract local features incapable of representing the global semantic information, which is especially important for solving large transformations. To address the discussed issues, we propose SAMConvex, a fast coarse-to-fine discrete optimization method for CT registration that includes a decoupled convex optimization procedure to obtain deformation fields based on a self-supervised anatomical embedding (SAM) feature extractor that captures both local and global information. To be specific, SAMConvex extracts per-voxel features and builds 6D correlation volumes based on SAM features, and iteratively updates a flow field by performing lookups on the correlation volumes with a coarse-to-fine scheme. SAMConvex outperforms the state-of-the-art learning-based methods and optimization-based methods over two inter-patient registration datasets (Abdomen CT and HeadNeck CT) and one intra-patient registration dataset (Lung CT). Moreover, as an optimization-based method, SAMConvex only takes similar to 2 s (similar to 5 s with instance optimization) for one paired images.

关键词： medical image Registration Large Deformation

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