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

共 2694 条记录，以下是231-240 订阅

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Learning Expected Appearances for Intraoperative Registration During Neurosurgery 26th

Learning Expected Appearances for Intraoperative Registratio...

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

作者： Haouchine, Nazim Dorent, Reuben Juvekar, Parikshit Torio, Erickson Wells, William M., III Kapur, Tina Golby, Alexandra J. Frisken, Sarah Harvard Med Sch Brigham & Womens Hosp Boston MA 02115 USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9783031439957;9783031439964

We present a novel method for intraoperative patient-to-image registration by learning Expected Appearances. Our method uses preoperative imaging to synthesize patient-specific expected views through a surgical microscope for a predicted range of transformations. Our method estimates the camera pose by minimizing the dissimilarity between the intraoperative 2D view through the optical microscope and the synthesized expected texture. In contrast to conventional methods, our approach transfers the processing tasks to the preoperative stage, reducing thereby the impact of low-resolution, distorted, and noisy intraoperative images, that often degrade the registration accuracy. We applied our method in the context of neuronavigation during brain surgery. We evaluated our approach on synthetic data and on retrospective data from 6 clinical cases. Our method outperformed state-of-the-art methods and achieved accuracies that met current clinical standards.

关键词： Intraoperative Registration image-guided Neurosurgery Augmented Reality Neural image Analogy 3D Pose Estimation

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Treasure in Distribution: A Domain Randomization Based Multi-source Domain Generalization for 2D medical image Segmentation 26th

Treasure in Distribution: A Domain Randomization Based Multi...

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

作者： Chen, Ziyang Pan, Yongsheng Ye, Yiwen Cui, Hengfei Xia, Yong Northwestern Polytech Univ Sch Comp Sci & Engn Natl Engn Lab Integrated Aerosp Ground Ocean Big Xian 710072 Peoples R China ShanghaiTech Univ Sch Biomed Engn Shanghai 201210 Peoples R China

ISBN: (纸本)9783031439001;9783031439018

Although recent years have witnessed the great success of convolutional neural networks (CNNs) in medical image segmentation, the domain shift issue caused by the highly variable image quality of medical images hinders the deployment of CNNs in real-world clinical applications. Domain generalization (DG) methods aim to address this issue by training a robust model on the source domain, which has a strong generalization ability. Previously, many DG methods based on feature-space domain randomization have been proposed, which, however, suffer from the limited and unordered search space of feature styles. In this paper, we propose a multi-source DG method called Treasure in Distribution (TriD), which constructs an unprecedented search space to obtain the model with strong robustness by randomly sampling from a uniform distribution. To learn the domain-invariant representations explicitly, we further devise a style-mixing strategy in our TriD, which mixes the feature styles by randomly mixing the augmented and original statistics along the channel wise and can be extended to other DG methods. Extensive experiments on two medical segmentation tasks with different modalities demonstrate that our TriD achieves superior generalization performance on unseen target-domain data. Code is available at https://***/Chen-Ziyang/TriD.

关键词： Domain generalization Domain randomization medical image segmentation Deep learning

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Investigating Data Memorization in 3D Latent Diffusion Models for medical image Synthesis 3rd

Investigating Data Memorization in 3D Latent Diffusion Model...

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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： Dar, Salman Ul Hassan Ghanaat, Arman Kahmann, Jannik Ayx, Isabelle Papavassiliu, theano Schoenberg, Stefan O. Engelhardt, Sandy Heidelberg Univ Hosp Dept Internal Med 3 Grp Artificial Intelligence CardiovascularMed D-69120 Heidelberg Germany AI Hlth Innovat Cluster Heidelberg Germany German Ctr Cardiovasc Res DZHK Partner Site Heidelberg Mannheim Heidelberg Germany Heidelberg Univ Univ Med Ctr Mannheim Dept Radiol & Nucl Med Theodor Kutzer Ufer 1-3 D-68167 Mannheim Germany Univ Med Ctr Mannheim Dept Med Cardiol 1 Theodor Kutzer Ufer 1-3 D-68167 Mannheim Germany

ISBN: (纸本)9783031537660;9783031537677

Generative latent diffusion models have been established as state-of-the-art in data generation. One promising application is generation of realistic synthetic medical imaging data for open data sharing without compromising patient privacy. Despite the promise, the capacity of such models to memorize sensitive patient training data and synthesize samples showing high resemblance to training data samples is relatively unexplored. Here, we assess the memorization capacity of 3D latent diffusion models on photon-counting coronary computed tomography angiography and knee magnetic resonance imaging datasets. To detect potential memorization of training samples, we utilize self-supervised models based on contrastive learning. Our results suggest that such latent diffusion models indeed memorize training data, and there is a dire need for devising strategies to mitigate memorization.

关键词： Deep generative models Latent diffusion Data memorization Patient privacy Contrastive learning

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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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MRIS: A Multi-modal Retrieval Approach for image Synthesis on Diverse Modalities 26th

MRIS: A Multi-modal Retrieval Approach for Image Synthesis o...

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

作者： Chen, Boqi Niethammer, Marc Univ N Carolina Dept Comp Sci Chapel Hill NC 27599 USA

ISBN: (纸本)9783031439988;9783031439995

Multiple imaging modalities are often used for disease diagnosis, prediction, or population-based analyses. However, not all modalities might be available due to cost, different study designs, or changes in imaging technology. If the differences between the types of imaging are small, data harmonization approaches can be used;for larger changes, direct image synthesis approaches have been explored. In this paper, we develop an approach, MRIS, based on multi-modal metric learning to synthesize images of diverse modalities. We use metric learning via multi-modal image retrieval, resulting in embeddings that can relate images of different modalities. Given a large image database, the learned image embeddings allow us to use k-nearest neighbor (k-NN) regression for image synthesis. Our driving medical problem is knee osteoarthritis (KOA), but our developed method is general after proper image alignment. We test our approach by synthesizing cartilage thickness maps obtained from 3D magnetic resonance (MR) images using 2D radiographs. Our experiments show that the proposed method outperforms direct image synthesis and that the synthesized thickness maps retain information relevant to downstream tasks such as progression prediction and Kellgren-Lawrence grading (KLG). Our results suggest that retrieval approaches can be used to obtain high-quality and meaningful image synthesis results given large image databases. Our code is available at https://***/uncbiag/MRIS.

关键词： Metric learning k-nearest neighbor osteoarthritis

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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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Additional Positive Enables Better Representation Learning for medical images 26th

Additional Positive Enables Better Representation Learning f...

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

作者： Zeng, Dewen Wu, Yawen Hu, Xinrong Xu, Xiaowei Hu, Jingtong Shi, Yiyu Univ Notre Dame Notre Dame IN 46556 USA Univ Pittsburgh Pittsburgh PA USA Guangdong Prov Peoples Hosp Guangzhou Peoples R China

ISBN: (纸本)9783031439063;9783031439070

this paper presents a new way to identify additional positive pairs for BYOL, a state-of-the-art (SOTA) self-supervised learning framework, to improve its representation learning ability. Unlike conventional BYOL which relies on only one positive pair generated by two augmented views of the same image, we argue that information from different images with the same label can bring more diversity and variations to the target features, thus benefiting representation learning. To identify such pairs without any label, we investigate TracIn, an instance-based and computationally efficient influence function, for BYOL training. Specifically, TracIn is a gradient-based method that reveals the impact of a training sample on a test sample in supervised learning. We extend it to the self-supervised learning setting and propose an efficient batchwise per-sample gradient computation method to estimate the pairwise TracIn for representing the similarity of samples in the mini-batch during training. For each image, we select the most similar sample from other images as the additional positive and pull their features together with BYOL loss. Experimental results on two public medical datasets (i.e., ISIC 2019 and ChestX-ray) demonstrate that the proposed method can improve the classification performance compared to other competitive baselines in both semi-supervised and transfer learning settings.

关键词： self-supervised learning representation learning medical image classification

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ConvFormer: Plug-and-Play CNN-Style Transformers for Improving medical image Segmentation 26th

ConvFormer: Plug-and-Play CNN-Style Transformers for Improvi...

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

作者： Lin, Xian Yan, Zengqiang Deng, Xianbo Zheng, Chuansheng Yu, Li Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan Peoples R China Huazhong Univ Sci & Technol Union Hosp Dept Radiol Tongji Med Coll Wuhan Peoples R China

ISBN: (纸本)9783031439001;9783031439018

Transformers have been extensively studied in medical image segmentation to build pairwise long-range dependence. Yet, relatively limited well-annotated medical image data makes transformers struggle to extract diverse global features, resulting in attention collapse where attention maps become similar or even identical. Comparatively, convolutional neural networks (CNNs) have better convergence properties on small-scale training data but suffer from limited receptive fields. Existing works are dedicated to exploring the combinations of CNN and transformers while ignoring attention collapse, leaving the potential of transformers under-explored. In this paper, we propose to build CNN-style Transformers (ConvFormer) to promote better attention convergence and thus better segmentation performance. Specifically, ConvFormer consists of pooling, CNN-style self-attention (CSA), and convolutional feed-forward network (CFFN) corresponding to tokenization, self-attention, and feed-forward network in vanilla vision transformers. In contrast to positional embedding and tokenization, ConvFormer adopts 2D convolution and max-pooling for both position information preservation and feature size reduction. In this way, CSA takes 2D feature maps as inputs and establishes long-range dependency by constructing self-attention matrices as convolution kernels with adaptive sizes. Following CSA, 2D convolution is utilized for feature refinement through CFFN. Experimental results on multiple datasets demonstrate the effectiveness of ConvFormer working as a plug-and-play module for consistent performance improvement of transformer-based frameworks. Code is available at https://***/xianlin7/ConvFormer.

关键词： CNN-Style Transformers Attention Collapse Adaptive Self-Attention medical image Segmentation

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MultiTalent: A Multi-dataset Approach to medical image Segmentation 1

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

作者： Ulrich, Constantin Isensee, Fabian Wald, Tassilo Zenk, Maximilian Baumgartner, Michael Maier-Hein, Klaus H. German Canc Res Ctr Div Med Image Comp Heidelberg Germany DKFZ Helmholtz Imaging Heidelberg Germany Heidelberg Univ Hosp Dept Radiat Oncol Pattern Anal & Learning Grp Heidelberg Germany NCT Heidelberg Natl Ctr Tumor Dis NCT Heidelberg Germany Heidelberg Univ Med Fac Heidelberg Heidelberg Germany Heidelberg Univ Fac Math & Comp Sci Heidelberg Germany

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

the medical imaging community generates a wealth of datasets, many of which are openly accessible and annotated for specific diseases and tasks such as multi-organ or lesion segmentation. Current practices continue to limit model training and supervised pre-training to one or a few similar datasets, neglecting the synergistic potential of other available annotated data. We propose MultiTalent, a method that leverages multiple CT datasets with diverse and conflicting class definitions to train a single model for a comprehensive structure segmentation. Our results demonstrate improved segmentation performance compared to previous related approaches, systematically, also compared to single-dataset training using state-of-the-art methods, especially for lesion segmentation and other challenging structures. We show that MultiTalent also represents a powerful foundation model that offers a superior pre-training for various segmentation tasks compared to commonly used supervised or unsupervised pre-training baselines. Our findings offer a new direction for the medical imaging community to effectively utilize the wealth of available data for improved segmentation performance. the code and model weights will be published here: https://***/ MIC- DKFZ/MultiTalent.

关键词： medical image segmentation multitask learning transfer learning foundation model partially labeled datasets

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