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检索条件"任意字段=8th International Conference on Medical Image Computing and Computer-Assisted Intervention"

共 2671 条记录，以下是151-160 订阅

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RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection 26th

RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain...

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

作者： Kang, Ming Ting, Chee-Ming Ting, Fung Fung Phan, Raphael C. -W. Monash Univ Sch Informat Technol Malaysia Campus Subang Jaya Malaysia

ISBN: (纸本)9783031439001;9783031439018

With an excellent balance between speed and accuracy, cutting-edge YOLO frameworks have become one of the most efficient algorithms for object detection. However, the performance of using YOLO networks is scarcely investigated in brain tumor detection. We propose a novel YOLO architecture with Reparameterized Convolution based on channel Shuffle (RCS-YOLO). We present RCS and a One-Shot Aggregation of RCS (RCS-OSA), which link feature cascade and computation efficiency to extract richer information and reduce time consumption. Experimental results on the brain tumor dataset Br35H show that the proposed model surpasses YOLOv6, YOLOv7, and YOLOv8 in speed and accuracy. Notably, compared with YOLOv7, the precision of RCS-YOLO improves by 1%, and the inference speed by 60% at 114.8 images detected per second (FPS). Our proposed RCS-YOLO achieves state-of-the-art performance on the brain tumor detection task. the code is available at https://***/mkang315/RCS-YOLO.

关键词： medical image detection YOLO Reparameterized convolution Channel shuffle Computation efficiency

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CXR-CLIP: Toward Large Scale Chest X-ray Language-image Pre-training 26th

CXR-CLIP: Toward Large Scale Chest X-ray Language-Image Pre-...

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

作者： You, Kihyun Gu, Jawook Ham, Jiyeon Park, Beomhee Kim, Jiho Hong, Eun K. Baek, Woonhyuk Roh, Byungseok Kakaobrain Seongnam South Korea

ISBN: (纸本)9783031438943;9783031438950

A large-scale image-text pair dataset has greatly contributed to the development of vision-language pre-training (VLP) models, which enable zero-shot or few-shot classification without costly annotation. However, in the medical domain, the scarcity of data remains a significant challenge for developing a powerful VLP model. In this paper, we tackle the lack of image-text data in chest X-ray by expanding image-label pair as image-text pair via general prompt and utilizing multiple images and multiple sections in a radiologic report. We also design two contrastive losses, named ICL and TCL, for learning study-level characteristics of medical images and reports, respectively. Our model outperforms the state-of-the-art models trained under the same conditions. Also, enlarged dataset improve the discriminative power of our pre-trained model for classification, while sacrificing marginal retrieval performance. Code is available at https://***/kakaobrain/cxr-clip.

关键词： Chest X-ray Vision-Language Pre-training Contrastive Learning

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Metrics to Quantify Global Consistency in Synthetic medical images 3rd

Metrics to Quantify Global Consistency in Synthetic Medical ...

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3rd Workshop on Deep Generative Models for medical image computing and computer assisted intervention (DGM4MICCAI) at the 26th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Scholz, Daniel Wiestler, Benedikt Rueckert, Daniel Menten, Martin J. Tech Univ Munich Lab AI Med Munich Germany Tech Univ Munich Klinikum Rechts Isar Dept Neuroradiol Munich Germany Imperial Coll London Dept Comp BioMedIA London England

ISBN: (纸本)9783031537660;9783031537677

image synthesis is increasingly being adopted in medical image processing, for example for data augmentation or inter-modality image translation. In these critical applications, the generated images must fulfill a high standard of biological correctness. A particular requirement for these images is global consistency, i.e an image being overall coherent and structured so that all parts of the image fit together in a realistic and meaningful way. Yet, established image quality metrics do not explicitly quantify this property of synthetic images. In this work, we introduce two metrics that can measure the global consistency of synthetic images on a per-image basis. To measure the global consistency, we presume that a realistic image exhibits consistent properties, e.g., a person's body fat in a whole-body MRI, throughout the depicted object or scene. Hence, we quantify global consistency by predicting and comparing explicit attributes of images on patches using supervised trained neural networks. Next, we adapt this strategy to an unlabeled setting by measuring the similarity of implicit image features predicted by a self-supervised trained network. Our results demonstrate that predicting explicit attributes of synthetic images on patches can distinguish globally consistent from inconsistent images. Implicit representations of images are less sensitive to assess global consistency but are still serviceable when labeled data is unavailable. Compared to established metrics, such as the FID, our method can explicitly measure global consistency on a per-image basis, enabling a dedicated analysis of the biological plausibility of single synthetic images.

关键词： Generative Modeling Synthetic images image Quality Metrics Global Consistency

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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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Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for medical image Classification 9th

Probing the Efficacy of Federated Parameter-Efficient Fine-T...

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

作者： Alkhunaizi, Naif Almalik, Faris Al-Refai, Rouqaiah Naseer, Muzammal Nandakumar, Karthik Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9783031776090;9783031776106

With the advent of large pre-trained transformer models, fine-tuning these models for various downstream tasks is a critical problem. Paucity of training data, the existence of data silos, and stringent privacy constraints exacerbate this fine-tuning problem in the medical imaging domain, creating a strong need for algorithms that enable collaborative fine-tuning of pre-trained models. Moreover, the large size of these models necessitates the use of parameter-efficient fine-tuning (PEFT) to reduce the communication burden in federated learning. In this work, we systematically investigate various federated PEFT strategies for adapting a Vision Transformer (ViT) model (pre-trained on a large natural image dataset) for medical image classification. Apart from evaluating known PEFT techniques, we introduce new federated variants of PEFT algorithms such as visual prompt tuning (VPT), low-rank decomposition of visual prompts, stochastic block attention fine-tuning, and hybrid PEFT methods like low-rank adaptation (LoRA)+VPT. Moreover, we perform a thorough empirical analysis to identify the optimal PEFT method for the federated setting and understand the impact of data distribution on federated PEFT, especially for out-of-domain (OOD) and non-IID data. the key insight of this study is that while most federated PEFT methods work well for in-domain transfer, there is a substantial accuracy vs. efficiency trade-off when dealing with OOD and non-IID scenarios, which is commonly the case in medical imaging. Specifically, every order of magnitude reduction in fine-tuned/exchanged parameters can lead to a 4% drop in accuracy. thus, the choice of the initial model is critical for the effectiveness of federated PEFT - rather than starting with general vision models, it is preferable to use medical foundation models (if available) learned using in-domain medical image data. Code: https://***/Naiftt/PEFT.

关键词： Vision Transformers Parameter-Efficient Fine-tuning Out-of-Domain Transfer Federated Learning

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Anti-adversarial Consistency Regularization for Data Augmentation: Applications to Robust medical image Segmentation 26th

Anti-adversarial Consistency Regularization for Data Augment...

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

作者： Cho, Hyuna Han, Yubin Kim, Won Hwa Pohang Univ Sci & Technol POSTECH Pohang South Korea

ISBN: (纸本)9783031439001;9783031439018

Modern deep learning methods for semantic segmentation require labor-intensive labeling for large-scale datasets with dense pixel-level annotations. Recent data augmentation methods such as dropping, mixing image patches, and adding random noises suggest effective ways to address the labeling issues for natural images. However, they can only be restrictively applied to medical image segmentation as they carry risks of distorting or ignoring the underlying clinical information of local regions of interest in an image. In this paper, we propose a novel data augmentation method for medical image segmentation without losing the semantics of the key objects (e.g., polyps). this is achieved by perturbing the objects with quasi-imperceptible adversarial noises and training a network to expand discriminative regions with a guide of anti-adversarial noises. Such guidance can be realized by a consistency regularization between the two contrasting data, and the strength of regularization is automatically and adaptively controlled considering their prediction uncertainty. Our proposed method significantly outperforms various existing methods with high sensitivity and Dice scores and extensive experiment results with multiple backbones on two datasets validate its effectiveness.

关键词： Adversarial attack and defense Data augmentation Semantic segmentation

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Hessian-Based Similarity Metric for Multimodal medical image Registration 26th

Hessian-Based Similarity Metric for Multimodal Medical Image...

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

作者： Eskandari, Mohammadreza Gueziri, Houssem-Eddine Collins, D. Louis McGill Univ Dept Biomed Engn Montreal PQ Canada Montreal Neurol Hosp & Inst McConnell Brain Imaging Ctr Montreal PQ Canada McGill Univ Dept Neurol & Neurosurg Montreal PQ Canada

ISBN: (纸本)9783031474248;9783031474255

One of the fundamental elements of both traditional and certain deep learning medical image registration algorithms is measuring the similarity/dissimilarity between two images. In this work, we propose an analytical solution for measuring similarity between two different medical image modalities based on the Hessian of their intensities. First, assuming a functional dependence between the intensities of two perfectly corresponding patches, we investigate how their Hessians relate to each other. Secondly, we suggest a closed-form expression to quantify the deviation from this relationship, given arbitrary pairs of image patches. We propose a geometrical interpretation of the new similarity metric and an efficient implementation for registration. We demonstrate the robustness of the metric to intensity nonuniformities using synthetic bias fields. By integrating the new metric in an affine registration framework, we evaluate its performance for MRI and ultrasound registration in the context of image-guided neurosurgery using target registration error and computation time.

关键词： Multimodal registration Similarity metric image Hessian

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Conditional Diffusion Models for Weakly Supervised medical image Segmentation 26th

Conditional Diffusion Models for Weakly Supervised Medical I...

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

作者： Hu, Xinrong Chen, Yu-Jen Ho, Tsung-Yi Shi, Yiyu Univ Notre Dame Notre Dame IN 46556 USA Natl Tsing Hua Univ Hsinchu Taiwan Chinese Univ Hong Kong Shatin Hong Kong Peoples R China

ISBN: (纸本)9783031439001;9783031439018

Recent advances in denoising diffusion probabilistic models have shown great success in image synthesis tasks. While there are already works exploring the potential of this powerful tool in image semantic segmentation, its application in weakly supervised semantic segmentation (WSSS) remains relatively under-explored. Observing that conditional diffusion models (CDM) is capable of generating images subject to specific distributions, in this work, we utilize category-aware semantic information underlied in CDM to get the prediction mask of the target object with only image-level annotations. More specifically, we locate the desired class by approximating the derivative of the output of CDM w.r.t the input condition. Our method is different from previous diffusion model methods with guidance from an external classifier, which accumulates noises in the background during the reconstruction process. Our method outperforms state-of-the-art CAM and diffusion model methods on two public medical image segmentation datasets, which demonstrates that CDM is a promising tool in WSSS. Also, experiment shows our method is more time-efficient than existing diffusion model methods, making it practical for wider applications. the codes are available at https://***/xhu248/cond_ddpm_wsss.

关键词： weakly supervised semantic segmentation diffusion models brain tumor magnetic resonance imaging

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Semi-supervised Domain Adaptive medical image Segmentation through Consistency Regularized Disentangled Contrastive Learning 26th

Semi-supervised Domain Adaptive Medical Image Segmentation T...

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

作者： Basak, Hritam Yin, Zhaozheng SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA

ISBN: (纸本)9783031439001;9783031439018

Although unsupervised domain adaptation (UDA) is a promising direction to alleviate domain shift, they fall short of their supervised counterparts. In this work, we investigate relatively less explored semi-supervised domain adaptation (SSDA) for medical image segmentation, where access to a few labeled target samples can improve the adaptation performance substantially. Specifically, we propose a two-stage training process. First, an encoder is pre-trained in a self-learning paradigm using a novel domain-content disentangled contrastive learning (CL) along with a pixel-level feature consistency constraint. the proposed CL enforces the encoder to learn discriminative content-specific but domain-invariant semantics on a global scale from the source and target images, whereas consistency regularization enforces the mining of local pixel-level information by maintaining spatial sensitivity. this pre-trained encoder, along with a decoder, is further fine-tuned for the downstream task, (i.e. pixel-level segmentation) using a semi-supervised setting. Furthermore, we experimentally validate that our proposed method can easily be extended for UDA settings, adding to the superiority of the proposed strategy. Upon evaluation on two domain adaptive image segmentation tasks, our proposed method outperforms the SoTA methods, both in SSDA and UDA settings. Code is available at Github.

关键词： Contrastive Learning Style-content disentanglement Consistency Regularization Domain Adaptation Segmentation

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A2FSeg: Adaptive Multi-modal Fusion Network for medical image Segmentation 26th

A2FSeg: Adaptive Multi-modal Fusion Network for Medical Imag...

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

作者： Wang, Zirui Hong, Yi Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9783031439001;9783031439018

Magnetic Resonance Imaging (MRI) plays an important role in multi-modal brain tumor segmentation. However, missing modality is very common in clinical diagnosis, which will lead to severe segmentation performance degradation. In this paper, we propose a simple adaptive multi-modal fusion network for brain tumor segmentation, which has two stages of feature fusion, including a simple average fusion and an adaptive fusion based on an attention mechanism. Both fusion techniques are capable to handle the missing modality situation and contribute to the improvement of segmentation results, especially the adaptive one. We evaluate our method on the BraTS2020 dataset, achieving the state-of-the-art performance for the incomplete multi-modal brain tumor segmentation, compared to four recent methods. Our A2FSeg (Average and Adaptive Fusion Segmentation network) is simple yet effective and has the capability of handling any number of image modalities for incomplete multi-modal segmentation. Our source code is online and available at https://***/Zirui0623/***.

关键词： Modality-adaptive fusion Missing modality Brain tumor segmentation Incomplete multi-modal segmentation

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