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

共 2692 条记录，以下是531-540 订阅

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Unsupervised Cross-Domain Feature Extraction for Single Blood Cell image Classification 1

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Salehi, Raheleh Sadafi, Ario Gruber, Armin Lienemann, Peter Navab, Nassir Albarqouni, Shadi Marr, Carsten Helmholtz Munich German Res Ctr Environm Hlth Inst AI Hlth Neuherberg Germany Politecnico Torino Comp Engn Turin Italy Tech Univ Munich Comp Aided Med Procedures CAMP Munich Germany Johns Hopkins Univ Comp Aided Med Procedures Baltimore MD USA Univ Hosp Bonn Clin Intervent & Diagnost Radiol Bonn Germany Tech Univ Munich Fac Informat Munich Germany Helmholtz Munich German Res Ctr Environm Hlth Neuherberg Germany

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Diagnosing hematological malignancies requires identification and classification of white blood cells in peripheral blood smears. Domain shifts caused by different lab procedures, staining, illumination, and microscope settings hamper the re-usability of recently developed machine learning methods on data collected from different sites. Here, we propose a cross-domain adapted autoencoder to extract features in an unsupervised manner on three different datasets of single white blood cells scanned from peripheral blood smears. the autoencoder is based on an R-CNN architecture allowing it to focus on the relevant white blood cell and eliminate artifacts in the image. To evaluate the quality of the extracted features we use a simple random forest to classify single cells. We show that thanks to the rich features extracted by the autoencoder trained on only one of the datasets, the random forest classifier performs satisfactorily on the unseen datasets, and outperforms published oracle networks in the cross-domain task. Our results suggest the possibility of employing this unsupervised approach in more complicated diagnosis and prognosis tasks without the need to add expensive expert labels to unseen data.

关键词： Unsupervised learning Feature extraction Autoencoders Single cell classification Microscopy Domain adaptation

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Over-the-RIS Modulation: A Reconfigurable Intelligent Surface assisted Modulation Design 8

Over-the-RIS Modulation: A Reconfigurable Intelligent Surfac...

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8th international conference on Communication, image and Signal Processing, CCISP 2023

作者： Huang, Yiqian Zammit, Saviour Yang, Ping Xiao, Yue Zhang, Bo Chen, Jienan Xiang, Wei University of Electronic Science and Technology of China National Key Laboratory of Sciencec and Technology on Communications Sichuan China University of Malta Department of Communications and Computer Engineering Msida Malta 32806 Troops Beijing China School of Computing Engineering and Mathematical Sciences La Trobe University Melbourne Australia

ISBN: (纸本)9798350305838

this paper proposes an improved over the air modulation (OTAM) scheme by using the powerful reconfigurable intelligent surfaces (RISs), namely over-the-RIS modulation (ORM). In the design framework, we reconstruct multipath channels by employing several RISs to increase the attenuation difference between multiple paths. Compared with conventional OTAM, ORM can be used in more generic fading scenarios, e.g., channels with non-ideal attenuation properties, and thus extends the application of OTAM. Based on the Bayesian criterion, we propose an optimal likelihood detector for the ORM system and derive a closed-form expression of the bit error rate (BER). Furthermore, we investigate the phase optimization of the RISs to improve its BER and deduce its closed-form solutions. Simulation results are presented to show that the proposed ORM system can achieve a better BER performance and higher transmission rate than the conventional OTAM systems, especially for environments with severe shadow fading. © 2023 IEEE.

关键词： Bit error rate

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A Novel Knowledge Keeper Network for 7T-Free but 7T-Guided Brain Tissue Segmentation 25th

A Novel Knowledge Keeper Network for 7T-Free but 7T-Guided B...

引用

25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Lee, Jieun Oh, Kwanseok Shen, Dinggang Suk, Heung-Il Korea Univ Dept Artificial Intelligence Seoul South Korea Korea Univ Dept Brain & Cognit Engn Seoul South Korea ShanghaiTech Univ Sch Biomed Engn Shanghai Peoples R China Shanghai United Imaging Intelligence Co Ltd Shanghai Peoples R China

ISBN: (纸本)9783031164439;9783031164422

An increase in signal-to-noise ratio (SNR) and susceptibility-induced contrast at higher field strengths, e.g., 7T, is crucial for medical image analysis by providing better insights for the pathophysiology, diagnosis, and treatment of several disease entities. However, it is difficult to obtain 7T images in real clinical practices due to the high cost and low accessibility. In this paper, we propose a novel knowledge keeper network (KKN) to guide brain tissue segmentation by taking advantage of 7T representations without explicitly using 7T images. By extracting features of a 3T input image substantially and then transforming them to 7T features via knowledge distillation (KD), our method achieves deriving 7T-like representations from a given 3T image and exploits them for tissue segmentation. On two independent datasets, we evaluated our method's validity in qualitative and quantitative manners on 7T-like image synthesis and 7T-guided tissue segmentation by comparing with the comparative methods in the literature.

关键词： Brain tissue segmentation Knowledge distillation medical image synthesis 7T MRI

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Non-iterative Coarse-to-Fine Registration Based on Single-Pass Deep Cumulative Learning 25th

Non-iterative Coarse-to-Fine Registration Based on Single-Pa...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Univ Sydney Sch Comp Sci Sydney Australia Shanghai Jiao Tong Univ Med X Res Inst Shanghai Peoples R China

ISBN: (纸本)9783031164460;9783031164453

Deformable image registration is a crucial step in medical image analysis for finding a non-linear spatial transformation between a pair of fixed and moving images. Deep registration methods based on Convolutional Neural Networks (CNNs) have been widely used as they can perform image registration in a fast and end-to-end manner. However, these methods usually have limited performance for image pairs with large deformations. Recently, iterative deep registration methods have been used to alleviate this limitation, where the transformations are iteratively learned in a coarse-to-fine manner. However, iterative methods inevitably prolong the registration runtime, and tend to learn separate image features for each iteration, which hinders the features from being leveraged to facilitate the registration at later iterations. In this study, we propose a Non-Iterative Coarse-to-finE registration Network (NICE-Net) for deformable image registration. In the NICE-Net, we propose: (i) a Single-pass Deep Cumulative Learning (SDCL) decoder that can cumulatively learn coarse-to-fine transformations within a single pass (iteration) of the network, and (ii) a Selectively-propagated Feature Learning (SFL) encoder that can learn common image features for the whole coarse-to-fine registration process and selectively propagate the features as needed. Extensive experiments on six public datasets of 3D brain Magnetic Resonance Imaging (MRI) show that our proposed NICE-Net can outperform state-of-the-art iterative deep registration methods while only requiring similar runtime to non-iterative methods.

关键词： image registration Cumulative learning Large deformations

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Self-supervised Depth Estimation in Laparoscopic image Using 3D Geometric Consistency 25th

Self-supervised Depth Estimation in Laparoscopic Image Using...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Huang, Baoru Zheng, Jian-Qing Nguyen, Anh Xu, Chi Gkouzionis, Ioannis Vyas, Kunal Tuch, David Giannarou, Stamatia Elson, Daniel S. Imperial Coll London Hamlyn Ctr Robot Surg London England Imperial Coll London Dept Surg & Canc London England Univ Oxford Kennedy Inst Rheumatol Oxford England Univ Oxford Big Data Inst Oxford England Univ Liverpool Dept Comp Sci Liverpool Merseyside England Lightpoint Med Ltd Chesham England

ISBN: (纸本)9783031164491;9783031164484

Depth estimation is a crucial step for image-guided intervention in robotic surgery and laparoscopic imaging system. Since per-pixel depth ground truth is difficult to acquire for laparoscopic image data, it is rarely possible to apply supervised depth estimation to surgical applications. As an alternative, self-supervised methods have been introduced to train depth estimators using only synchronized stereo image pairs. However, most recent work focused on the left-right consistency in 2D and ignored valuable inherent 3D information on the object in real world coordinates, meaning that the left-right 3D geometric structural consistency is not fully utilized. To overcome this limitation, we present M3Depth, a self-supervised depth estimator to leverage 3D geometric structural information hidden in stereo pairs while keeping monocular inference. the method also removes the influence of border regions unseen in at least one of the stereo images via masking, to enhance the correspondences between left and right images in overlapping areas. Extensive experiments show that our method outperforms previous self-supervised approaches on both a public dataset and a newly acquired dataset by a large margin, indicating a good generalization across different samples and laparoscopes.

关键词： Self-supervised monocular depth estimation Laparoscopic images 3D geometric consistency

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Interaction-Oriented Feature Decomposition for medical image Lesion Detection 1

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Shen, Junyong Hu, Yan Zhang, Xiaoqing Qiu, Zhongxi Deng, Tingming Xu, Yanwu Liu, Jiang Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Baidu Intelligent Healthcare Unit Beijing 100000 Peoples R China Shenzhen Peoples Hosp Dept Ophthalmol Shenzhen 518020 Guangdong Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain inspired Intelligent Shenzhen 518055 Peoples R China

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Common lesion detection networks typically use lesion features for classification and localization. However, many lesions are classified only by lesion features without considering the relation with global context features, which raises the misclassification problem. In this paper, we propose an Interaction-Oriented Feature Decomposition (IOFD) network to improve the detection performance on context-dependent lesions. Specifically, we decompose features output from a backbone into global context features and lesion features that are optimized independently. then, we design two novel modules to improve the lesion classification accuracy. A Global Context Embedding (GCE) module is designed to extract global context features. A Global Context Cross Attention (GCCA) module without additional parameters is designed to model the interaction between global context features and lesion features. Besides, considering the different features required by classification and localization tasks, we further adopt a task decoupling strategy. IOFD is easy to train and end-to-end in terms of training and inference. the experimental results for datasets in two modalities outperform state-of-the-art algorithms, which demonstrates the effectiveness and generality of IOFD.

关键词： Lesion detection Context embedding Cross attention medical image

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Deep Multimodal Guidance for medical image Classification 25th

Deep Multimodal Guidance for Medical Image Classification

引用

25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Mallya, Mayur Hamarneh, Ghassan Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9783031164491;9783031164484

medical imaging is a cornerstone of therapy and diagnosis in modern medicine. However, the choice of imaging modality for a particular theranostic task typically involves trade-offs between the feasibility of using a particular modality (e.g., short wait times, low cost, fast acquisition, reduced radiation/invasiveness) and the expected performance on a clinical task (e.g., diagnostic accuracy, efficacy of treatment planning and guidance). In this work, we aim to apply the knowledge learned from the less feasible but better-performing (superior) modality to guide the utilization of the more-feasible yet under-performing (inferior) modality and steer it towards improved performance. We focus on the application of deep learning for image-based diagnosis. We develop a light-weight guidance model that leverages the latent representation learned from the superior modality, when training a model that consumes only the inferior modality. We examine the advantages of our method in the context of two clinical applications: multi-task skin lesion classification from clinical and dermoscopic images and brain tumor classification from multi-sequence magnetic resonance imaging (MRI) and histopathology images. For both these scenarios we show a boost in diagnostic performance of the inferior modality without requiring the superior modality. Furthermore, in the case of brain tumor classification, our method outperforms the model trained on the superior modality while producing comparable results to the model that uses both modalities during inference. We make our code and trained models available at: https://***/mayurmallya/DeepGuide.

关键词： Deep learning Multimodal learning Classification Student-teacher learning Knowledge distillation Skin lesions Brain tumors

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Adversarial Consistency for Single Domain Generalization in medical image Segmentation 25th

Adversarial Consistency for Single Domain Generalization in ...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Xu, Yanwu Xie, Shaoan Reynolds, Maxwell Ragoza, Matthew Gong, Mingming Batmanghelich, Kayhan Univ Pittsburgh Pittsburgh PA 15206 USA Univ Melbourne Sch Math & Stat Melbourne Australia Carnegie Mellon Univ Dept Philosophy Pittsburgh PA USA

ISBN: (纸本)9783031164491;9783031164484

An organ segmentation method that can generalize to unseen contrasts and scanner settings can significantly reduce the need for retraining of deep learning models. Domain Generalization (DG) aims to achieve this goal. However, most DG methods for segmentation require training data from multiple domains during training. We propose a novel adversarial domain generalization method for organ segmentation trained on data from a single domain. We synthesize the new domains via learning an adversarial domain synthesizer (ADS) and presume that the synthetic domains cover a large enough area of plausible distributions so that unseen domains can be interpolated from synthetic domains. We propose a mutual information regularizer to enforce the semantic consistency between images from the synthetic domains, which can be estimated by patch-level contrastive learning. We evaluate our method for various organ segmentation for unseen modalities, scanning protocols, and scanner sites.

关键词： medical image segmentation Single domain generalization Adversarial training Mutual information

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GradMix for Nuclei Segmentation and Classification in Imbalanced Pathology image Datasets 25th

GradMix for Nuclei Segmentation and Classification in Imbala...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Doan, Tan Nhu Nhat Kim, Kyungeun Song, Boram Kwak, Jin Tae Sejong Univ Dept Comp Sci & Engn Seoul 05006 South Korea Sungkyunkwan Univ Kangbuk Samsung Hosp Dept Pathol Sch Med Seoul 05505 South Korea Korea Univ Sch Elect Engn Seoul 02841 South Korea

ISBN: (纸本)9783031164347;9783031164330

An automated segmentation and classification of nuclei is an essential task in digital pathology. the current deep learning-based approaches require a vast amount of annotated datasets by pathologists. However, the existing datasets are imbalanced among different types of nuclei in general, leading to a substantial performance degradation. In this paper, we propose a simple but effective data augmentation technique, termed GradMix, that is specifically designed for nuclei segmentation and classification. GradMix takes a pair of a major-class nucleus and a rare-class nucleus, creates a customized mixing mask, and combines them using the mask to generate a new rare-class nucleus. As it combines two nuclei, GradMix considers both nuclei and the neighboring environment by using the customized mixing mask. this allows us to generate realistic rare-class nuclei with varying environments. We employed two datasets to evaluate the effectiveness of GradMix. the experimental results suggest that GradMix is able to improve the performance of nuclei segmentation and classification in imbalanced pathology image datasets.

关键词： Nuclei segmentation and classification Data augmentation Data imbalance

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Learning-Based and Unrolled Motion-Compensated Reconstruction for Cardiac MR CINE Imaging 25th

Learning-Based and Unrolled Motion-Compensated Reconstructio...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Pan, Jiazhen Rueckert, Daniel Kuestner, thomas Hammernik, Kerstin Tech Univ Munich Klinikum Rechts Isar Munich Germany Imperial Coll London Dept Comp London England Univ Hosp Tubingen Med Image & Data Anal MIDAS Lab Tubingen Germany

ISBN: (纸本)9783031164460;9783031164453

Motion-compensated MR reconstruction (MCMR) is a powerful concept with considerable potential, consisting of two coupled subproblems: Motion estimation, assuming a known image, and image reconstruction, assuming known motion. In this work, we propose a learning-based self-supervised framework for MCMR, to efficiently deal with nonrigid motion corruption in cardiac MR imaging. Contrary to conventional MCMR methods in which the motion is estimated prior to reconstruction and remains unchanged during the iterative optimization process, we introduce a dynamic motion estimation process and embed it into the unrolled optimization. We establish a cardiac motion estimation network that leverages temporal information via a group-wise registration approach, and carry out a joint optimization between the motion estimation and reconstruction. Experiments on 40 acquired 2D cardiac MR CINE datasets demonstrate that the proposed unrolled MCMR framework can reconstruct high quality MR images at high acceleration rates where other state-of-the-art methods fail. We also show that the joint optimization mechanism is mutually beneficial for both sub-tasks, i.e., motion estimation and image reconstruction, especially when the MR image is highly undersampled.

关键词： Motion estimation

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