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检索条件"任意字段=3rd International Conference on Medical Image Computing and Computer-Assisted Intervention"

共 4110 条记录，以下是121-130 订阅

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Learning Transferable Object-Centric Diffeomorphic Transformations for Data Augmentation in medical image Segmentation 26th

Learning Transferable Object-Centric Diffeomorphic Transform...

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

作者： Kumar, Nilesh Gyawali, Prashnna K. Ghimire, Sandesh Wang, Linwei Rochester Inst Technol Rochester NY 14623 USA West Virginia Univ Morgantown WV 26506 USA Qualcomm Inc San Diego CA USA

ISBN: (纸本)9783031438943;9783031438950

Obtaining labelled data in medical image segmentation is challenging due to the need for pixel-level annotations by experts. Recent works have shown that augmenting the object of interest with deformable transformations can help mitigate this challenge. However, these transformations have been learned globally for the image, limiting their transferability across datasets or applicability in problems where image alignment is difficult. While object-centric augmentations provide a great opportunity to overcome these issues, existing works are only focused on position and random transformations without considering shape variations of the objects. To this end, we propose a novel object-centric data augmentation model that is able to learn the shape variations for the objects of interest and augment the object in place without modifying the rest of the image. We demonstrated its effectiveness in improving kidney tumour segmentation when leveraging shape variations learned both from within the same dataset and transferred from external datasets.

关键词： Data Augmentation Diffeomorphic transformations image Segmentation

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Text-Guided Cross-Position Attention for Segmentation: Case of medical image 26th

Text-Guided Cross-Position Attention for Segmentation: Case ...

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

作者： Lee, Go-Eun Kim, Seon Ho Cho, Jungchan Choi, Sang Tae Choi, Sang-Il Dankook Univ Yongin Gyeonggi Do South Korea Univ Southern Calif Los Angeles CA 90007 USA Gachon Univ Seongnam Gyeonggi Do South Korea Chung Ang Univ Coll Med Seoul South Korea

ISBN: (纸本)9783031439032;9783031439049

We propose a novel text-guided cross-position attention module which aims at applying a multi-modality of text and image to position attention in medical image segmentation. To match the dimension of the text feature to that of the image feature map, we multiply learnable parameters by text features and combine the multi-modal semantics via cross-attention. It allows a model to learn the dependency between various characteristics of text and image. Our proposed model demonstrates superior performance compared to other medical models using image-only data or image-text data. Furthermore, we utilize our module as a region of interest (RoI) generator to classify the inflammation of the sacroiliac joints. The RoIs obtained from the model contribute to improve the performance of classification models.

关键词： image Segmentation Multi Modal Learning Cross Position Attention Text-Guided Attention medical image

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SwinUNETR-V2: Stronger Swin Transformers with Stagewise Convolutions for 3D medical image Segmentation 26th

SwinUNETR-V2: Stronger Swin Transformers with Stagewise Conv...

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

作者： He, Yufan Nath, Vishwesh Yang, Dong Tang, Yucheng Myronenko, Andriy Xu, Daguang NVidia Santa Clara CA 95051 USA

ISBN: (纸本)9783031439001;9783031439018

Transformers for medical image segmentation have attracted broad interest. Unlike convolutional networks (CNNs), transformers use self-attentions that do not have a strong inductive bias. This gives transformers the ability to learn long-range dependencies and stronger modeling capacities. Although they, e.g. SwinUNETR, achieve state-of-the-art (SOTA) results on some benchmarks, the lack of inductive bias makes transformers harder to train, requires much more training data, and are sensitive to training recipes. In many clinical scenarios and challenges, transformers can still have inferior performances than SOTA CNNs like nnUNet. A transformer backbone and corresponding training recipe, which can achieve top performances under different medical image segmentation scenarios, still needs to be developed. In this paper, we enhance the SwinUNETR with convolutions, which results in a surprisingly stronger backbone, the SwinUNETR-V2, for 3D medical image segmentation. It achieves top performance on a variety of benchmarks of different sizes and modalities, including the Whole abdominal ORgan Dataset (WOrd), MICCAI FLARE2021 dataset, MSD pancreas dataset, MSD prostate dataset, and MSD lung cancer dataset, all using the same training recipe (https://***/Project-MONAI/researchcontributions/tree/main/SwinUNETR/BTCV, our training recipe is the same as that by SwinUNETR) with minimum changes across tasks.

关键词： Swin transformer Convolution Hybrid model medical image segmentation

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11th international Workshop on Biomedical image Registration, WBIR 2024, held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

11th International Workshop on Biomedical Image Registration...

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11th international Workshop on Biomedical image Registration, WBIR 2024, held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

ISBN: (纸本)9783031734793

The proceedings contain 28 papers. The special focus in this conference is on Biomedical image Registration. The topics include: Large Deformation Registration with A Confidence-Guided Network;unleashing Registration: Diffusion Models for Synthetic Paired 3D Training Data;feedback Attention for Unsupervised Cardiac Motion Estimation in 3D Echocardiography;learning Deformable Intra-Patient Liver Registration with Graph Cross-Attention;mamba? Catch The Hype Or Rethink What Really Helps for image Registration;assessing the Robustness of image Registration Models Under Domain Shifts with Learnable Input images;challenging the Robustness of image Registration Similarity Metrics with Adversarial Attacks;Comparative Study on Co-registration Techniques for Diffusion-Weighted Breast MRI and Improved ADC Mapping;a Learning-Free Approach to Mitigate Abnormal Deformations in medical image Registration;Deformable MRI Sequence Registration for AI-Based Prostate Cancer Diagnosis;SINA: Sharp Implicit Neural Atlases by Joint Optimisation of Representation and Deformation;A Novel Fusion of CT/MRI and US images Based on Depth Camera and Electromagnetic Tracking;deep Learning Multi-channel Structural and Diffusion Tensor Neonatal image Registration;registration by Regression (RbR): A Framework for Interpretable and Flexible Atlas Registration;Diffusion Model-Based Hierarchical Registration of Whole-Body PET/CT images;unsupervised Similarity Learning for image Registration with Energy-Based Models;Segmentation by Registration-Enabled SAM Prompt Engineering Using Five Reference images;electron Microscopy image Registration with Twin Axial Transformer and Progressive Training;general Vision Encoder Features as Guidance in medical image Registration;rigid Single-Slice-in-Volume Registration via Rotation-Equivariant 2D/3D Feature Matching;a Self-supervised image Registration Approach for Measuring Local Response Patterns in Metastatic Ovarian Cancer;CAR: Contrast-Agnostic Deformable medical

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PMC-CLIP: Contrastive Language-image Pre-training Using Biomedical Documents 26th

PMC-CLIP: Contrastive Language-Image Pre-training Using Biom...

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

作者： Lin, Weixiong Zhao, Ziheng Zhang, Xiaoman Wu, Chaoyi Zhang, Ya Wang, Yanfeng Xie, Weidi Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9783031439926;9783031439933

Foundation models trained on large-scale dataset gain a recent surge in CV and NLP. In contrast, development in biomedical domain lags far behind due to data scarcity. To address this issue, we build and release PMC-OA, a biomedical dataset with 1.6M image-caption pairs collected from PubMedCentral's OpenAccess subset, which is 8 times larger than before, PMC-OA covers diverse modalities or diseases, with majority of the image-caption samples aligned at finer-grained level, i.e., subfigure and subcaption. While pretraining a CLIP-style model on PMC-OA, our model named PMC-CLIP outperform previous state-of-the-art models on various downstream tasks, including image-text retrieval on ROCO, MedMNIST image classification, medical VQA, for example, +8.1% R@10 on image-text retrieval, +3.9% accuracy on image classification.

关键词： Multimodal Dataset Vision-Language Pretraining

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Pre-trained Diffusion Models for Plug-and-Play medical image Enhancement 1

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

作者： Ma, Jun Zhu, Yuanzhi You, Chenyu Wang, Bo Univ Hlth Network Peter Munk Cardiac Ctr Toronto ON Canada Univ Toronto Dept Lab Med & Pathobiol Toronto ON Canada Vector Inst Artificial Intelligence Toronto ON Canada Swiss Fed Inst Technol Dept Informat Technol & Elect Engn Zurich Switzerland Yale Univ Dept Elect Engn New Haven CT USA Univ Toronto Dept Comp Sci Toronto ON Canada Univ Hlth Network AI Hub Toronto ON Canada

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Deep learning-based medical image enhancement methods (e.g., denoising and super-resolution) mainly rely on paired data and correspondingly the well-trained models can only handle one type of task. In this paper, we address the limitation with a diffusion model-based framework that mitigates the requirement of paired data and can simultaneously handle multiple enhancement tasks by one pre-trained diffusion model without fine-tuning. Experiments on low-dose CT and heart MR datasets demonstrate that the proposed method is versatile and robust for image denoising and super-resolution. We believe our work constitutes a practical and versatile solution to scalable and generalizable image enhancement.

关键词： image enhancement

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Co-learning Semantic-Aware Unsupervised Segmentation for Pathological image Registration 26th

Co-learning Semantic-Aware Unsupervised Segmentation for Pat...

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

作者： Liu, Yang Gu, Shi Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9783031439988;9783031439995

The registration of pathological images plays an important role in medical applications. Despite its significance, most researchers in this field primarily focus on the registration of normal tissue into normal tissue. The negative impact of focal tissue, such as the loss of spatial correspondence information and the abnormal distortion of tissue, are rarely considered. In this paper, we propose a novel unsupervised approach for pathological image registration by incorporating segmentation and inpainting. The registration, segmentation, and inpainting modules are trained simultaneously in a co-learning manner so that the segmentation of the focal area and the registration of inpainted pairs can improve collaboratively. Overall, the registration of pathological images is achieved in a completely unsupervised learning framework. Experimental results on multiple datasets, including Magnetic Resonance Imaging (MRI) of T1 sequences, demonstrate the efficacy of our proposed method. Our results show that our method can accurately achieve the registration of pathological images and identify lesions even in challenging imaging modalities. Our unsupervised approach offers a promising solution for the efficient and cost-effective registration of pathological images. Our code is available at https://***/brain-intelligence-lab/GIRNet.

关键词： Unsupervised Collaborative Learning Registration Segmentation Pathological image

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15th international Workshop on Machine Learning in medical Imaging, MLMI 2024 was held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

15th International Workshop on Machine Learning in Medical I...

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15th international Workshop on Machine Learning in medical Imaging, MLMI 2024 was held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

ISBN: (纸本)9783031732836

The proceedings contain 63 papers. The special focus in this conference is on Machine Learning in medical Imaging. The topics include: IRUM: An image Representation and Unified Learning Method for Breast Cancer Diagnosis from Multi-View Ultrasound images;Classification, Regression and Segmentation Directly from K-Space in Cardiac MRI;DDSB: An Unsupervised and Training-Free Method for Phase Detection in Echocardiography;mitral Regurgitation Recogniton Based on Unsupervised Out-of-Distribution Detection with Residual Diffusion Amplification;Deep Reinforcement Learning with Multiple Centerline-Guidance for Localization of Left Atrial Appendage Orifice from CT images;Lung-CADex: Fully Automatic Zero-Shot Detection and Classification of Lung Nodules in Thoracic CT images;CIResDiff: A Clinically-Informed Residual Diffusion Model for Predicting Idiopathic Pulmonary Fibrosis Progression;vision Transformer Model for Automated End-to-End Radiographic Assessment of Joint Damage in Psoriatic Arthritis;corticalEvolve: Age-Conditioned Ordinary Differential Equation Model for Cortical Surface Reconstruction;CSR-dMRI: Continuous Super-Resolution of Diffusion MRI with Anatomical Structure-assisted Implicit Neural Representation Learning;atherosclerotic Plaque Stability Prediction from Longitudinal Ultrasound images;Leveraging IHC Staining to Prompt HER2 Status Prediction from HE-Stained Histopathology Whole Slide images;VIMs: Virtual Immunohistochemistry Multiplex Staining via Text-to-Stain Diffusion Trained on Uniplex Stains;structural-Connectivity-Guided Functional Connectivity Representation for Multi-modal Brain Disease Classification;Clinical Brain MRI Super-Resolution with 2D Slice-Wise Diffusion Model;Low-to-High Frequency Progressive K-Space Learning for MRI Reconstruction;LSST: Learned Single-Shot Trajectory and Reconstruction Network for MR Imaging;7T-Like T1-Weighted and TOF MRI Synthesis from 3T MRI with Multi-contrast Complementary Deep Learning;A Probabilistic Hadamard

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SegNetr: Rethinking the Local-Global Interactions and Skip Connections in U-Shaped Networks 26th

SegNetr: Rethinking the Local-Global Interactions and Skip C...

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

作者： Cheng, Junlong Gao, Chengrui Wang, Fengjie Zhu, Min Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Recently, U-shaped networks have dominated the field of medical image segmentation due to their simple and easily tuned structure. However, existing U-shaped segmentation networks: 1) mostly focus on designing complex self-attention modules to compensate for the lack of long-term dependence based on convolution operation, which increases the overall number of parameters and computational complexity of the network;2) simply fuse the features of encoder and decoder, ignoring the connection between their spatial locations. In this paper, we rethink the above problem and build a lightweight medical image segmentation network, called SegNetr. Specifically, we introduce a novel SegNetr block that can perform local-global interactions dynamically at any stage and with only linear complexity. At the same time, we design a general information retention skip connection (IRSC) to preserve the spatial location information of encoder features and achieve accurate fusion with the decoder features. We validate the effectiveness of SegNetr on four mainstream medical image segmentation datasets, with 59% and 76% fewer parameters and GFLOPs than vanilla U-Net, while achieving segmentation performance comparable to state-of-the-art methods. Notably, the components proposed in this paper can be applied to other U-shaped networks to improve their segmentation performance.

关键词： Local-global interactions Information retention skip connection medical image segmentation U-shaped networks

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Dynamic Curriculum Learning via In-Domain Uncertainty for medical image Classification 26th

Dynamic Curriculum Learning via In-Domain Uncertainty for Me...

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

作者： Li, Chaoyi Li, Meng Peng, Can Lovell, Brian C. Univ Queensland Sch EECS St Lucia Qld 4072 Australia CSIRO DATA 61 Robot & Autonomous Syst Grp Pullenvale Australia

ISBN: (纸本)9783031439032;9783031439049

This paper presents an innovative approach to curriculum learning, which is a technique used to train learning models. Curriculum learning is inspired by the way humans learn, starting with simple examples and gradually progressing to more challenging ones. There are currently two main types of curriculum learning: fixed curriculum generated by transfer learning, and self-paced learning based on loss functions. However, these methods have limitations that can hinder their effectiveness. To overcome these limitations, this article proposes a new approach called Dynamic Curriculum Learning via In-Domain Uncertainty (DCLU), which is derived from uncertainty estimation. The proposed approach utilizes a Dirichlet distribution classifier to obtain prediction and uncertainty estimates from the network, which can be used as a metric to quantify the difficulty level of the data. An uncertainty-aware sampling pacing function is also introduced to adapt the curriculum according to the difficulty metric. This new approach has been evaluated on two medical image datasets, and the results show that it outperforms other curriculum learning methods. The source code for this approach will be released at https://***/Joey2117/DCLU.

关键词： medical image classification Curriculum learning Uncertainty estimation

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