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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361-370 订阅

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Deep Learning-Based Anonymization of Chest Radiographs: A Utility-Preserving Measure for Patient Privacy 1

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

作者： Packhaeuser, Kai Guendel, Sebastian thamm, Florian Denzinger, Felix Maier, Andreas Friedrich Alexander Univ Erlangen Nurnberg Erlangen Germany

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Robust and reliable anonymization of chest radiographs constitutes an essential step before publishing large datasets of such for research purposes. the conventional anonymization process is carried out by obscuring personal information in the images with black boxes and removing or replacing meta-information. However, such simple measures retain biometric information in the chest radiographs, allowing patients to be re-identified by a linkage attack. therefore, there is an urgent need to obfuscate the biometric information appearing in the images. We propose the first deep learning-based approach (PriCheXy-Net) to targetedly anonymize chest radiographs while maintaining data utility for diagnostic andmachine learning purposes. Ourmodel architecture is a composition of three independent neural networks that, when collectively used, allow for learning a deformation field that is able to impede patient re-identification. Quantitative results on the ChestX-ray14 dataset show a reduction of patient re-identification from 81.8% to 57.7% (AUC) after re-training with little impact on the abnormality classification performance. this indicates the ability to preserve underlying abnormality patterns while increasing patient privacy. Lastly, we compare our proposed anonymization approach with two other obfuscation-based methods (Privacy-Net, DP-Pix) and demonstrate the superiority of our method towards resolving the privacy-utility trade-off for chest radiographs.

关键词： image Anonymization Patient Privacy Data Utility

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IIB-MIL: Integrated Instance-Level and Bag-Level Multiple Instances Learning with Label Disambiguation for Pathological image Analysis 26th

IIB-MIL: Integrated Instance-Level and Bag-Level Multiple In...

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

作者： Ren, Qin Zhao, Yu He, Bing Wu, Bingzhe Mai, Sijie Xu, Fan Huang, Yueshan He, Yonghong Huang, Junzhou Yao, Jianhua Tencent AI Lab Shenzhen 518000 Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen 518071 Peoples R China Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510006 Peoples R China ShanghaiTech Univ Shanghai 201210 Peoples R China Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Univ Texas Arlington Arlington TX 76019 USA

ISBN: (纸本)9783031439865;9783031439872

Digital pathology plays a pivotal role in the diagnosis and interpretation of diseases and has drawn increasing attention in modern healthcare. Due to the huge gigapixel-level size and diverse nature of whole-slide images (WSIs), analyzing them through multiple instance learning (MIL) has become a widely-used scheme, which, however, faces the challenges that come with the weakly supervised nature of MIL. Conventional MIL methods mostly either utilized instance-level or bag-level supervision to learn informative representations from WSIs for downstream tasks. In this work, we propose a novel MIL method for pathological image analysis with integrated instance-level and bag-level supervision (termed IIB-MIL). More importantly, to overcome the weakly supervised nature of MIL, we design a label-disambiguation-based instance-level supervision for MIL using Prototypes and Confidence Bank to reduce the impact of noisy labels. Extensive experiments demonstrate that IIB-MIL outperforms state-of-the-art approaches in both benchmarking datasets and addressing the challenging practical clinical task. the code is available at https://***/TencentAILabHealthcare/IIB-MIL.

关键词： computational pathology multi-instance learning label disambiguation prototype confidence bank

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Morphology-Inspired Unsupervised Gland Segmentation via Selective Semantic Grouping 26th

Morphology-Inspired Unsupervised Gland Segmentation via Sele...

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

作者： Zhang, Qixiang Li, Yi Xue, Cheng Li, Xiaomeng Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9783031439001;9783031439018

Designing deep learning algorithms for gland segmentation is crucial for automatic cancer diagnosis and prognosis. However, the expensive annotation cost hinders the development and application of this technology. In this paper, we make a first attempt to explore a deep learning method for unsupervised gland segmentation, where no manual annotations are required. Existing unsupervised semantic segmentation methods encounter a huge challenge on gland images. they either over-segment a gland into many fractions or under-segment the gland regions by confusing many of them with the background. To overcome this challenge, our key insight is to introduce an empirical cue about gland morphology as extra knowledge to guide the segmentation process. To this end, we propose a novel Morphology-inspired method via Selective Semantic Grouping. We first leverage the empirical cue to selectively mine out proposals for gland sub-regions with variant appearances. then, a Morphology-aware Semantic Grouping module is employed to summarize the overall information about glands by explicitly grouping the semantics of their sub-region proposals. In this way, the final segmentation network could learn comprehensive knowledge about glands and produce well-delineated and complete predictions. We conduct experiments on the GlaS dataset and the CRAG dataset. Our method exceeds the second-best counterpart by over 10.56% at mIOU.

关键词： Whole Slide image Unsupervised Gland Segmentation Morphology-inspired Learning Semantic Grouping

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Identification of Disease-Sensitive Brain Imaging Phenotypes and Genetic Factors Using GWAS Summary Statistics 26th

Identification of Disease-Sensitive Brain Imaging Phenotypes...

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

作者： Xi, Duo Cui, Dingnan Zhang, Jin Shang, Muheng Zhang, Minjianan Guo, Lei Han, Junwei Du, Lei Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

ISBN: (纸本)9783031439032;9783031439049

Brain imaging genetics is a rapidly growing neuroscience area that integrates genetic variations and brain imaging phenotypes to investigate the genetic underpinnings of brain disorders. In this field, using multi-modal imaging data can leverage complementary information and thus stands a chance of identifying comprehensive genetic risk factors. Due to privacy and copyright issues, many imaging and genetic data are unavailable, and thus existing imaging genetic methods cannot work. In this paper, we proposed a novel multi-modal brain imaging genetic learning method that can study the associations between imaging phenotypes and genetic variations using genome-wide association study (GWAS) summary statistics. Our method leverages the powerful multi-modal of brain imaging phenotypes and GWAS. More importantly, it does not need to access the imaging and genetic data of each individual. Experimental results on both Alzheimer's Disease Neuroimaging Initiative (ADNI) database and GWAS summary statistics suggested that our method has the same learning ability, including identifying associations between genetic biomarkers and imaging phenotypes and selecting relevant biomarkers, as those counterparts depending on the individual data. therefore, our learning method provides a novel methodology for brain imaging genetics without individual data.

关键词： Brain imaging genetics GWAS summary statistics Multi-modal brain image analysis

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Federated Condition Generalization on Low-dose CT Reconstruction via Cross-domain Learning 1

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

作者： Chen, Shixuan Cao, Boxuan Du, Yinda Zhang, Yaoduo He, Ji Bian, Zhaoying Zeng, Dong Ma, Jianhua Southern Med Univ Sch Biomed Engn Guangzhou Guangdong Peoples R China Southern Med Univ Zhujiang Hosp Dept Radiol Guangzhou Guangdong Peoples R China Pazhou Lab Huangpu Guangzhou Guangdong Peoples R China Guangzhou Med Univ Sch Biomed Engn Guangzhou Guangdong Peoples R China

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

the harmful radiation dose associated with CT imaging is a major concern because it can cause genetic diseases. Acquiring CT data at low radiation doses has become a pressing goal. Deep learning (DL)-based methods have proven to suppress noise-induced artifacts and promote image quality in low-dose CT imaging. However, it should be noted that most of the DL-based methods are constructed based on the CT data from a specific condition, i.e., specific imaging geometry and specific dose level. then these methods might generalize poorly to the other conditions, i.e., different imaging geometries and other radiation doses, due to the big data heterogeneity. In this study, to address this issue, we propose a condition generalization method under a federated learning framework (FedCG) to reconstruct CT images on two conditions: three different dose levels and different sampling shcemes at three different geometries. Specifically, the proposed FedCG method leverages a cross-domain learning approach: individual-client sinogram learning and cross-client image reconstruction for condition generalization. In each individual client, the sinogram at each condition is processed similarly to that in the iRadonMAP. then the CT images at each client are learned via a condition generalization network in the server which considers latent common characteristics in the CT images at all conditions and preserves the client-specific characteristics in each condition. Experiments show that the proposed FedCG outperforms the other competing methods on two imaging conditions in terms of qualitative and quantitative assessments.

关键词： low-dose CT image reconstruction federal learning generalization generalization

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BigFUSE: Global Context-Aware image Fusion in Dual-View Light-Sheet Fluorescence Microscopy with image Formation Prior 26th

BigFUSE: Global Context-Aware Image Fusion in Dual-View Ligh...

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

作者： Liu, Yu Mueller, Gesine Navab, Nassir Marr, Carsten Huisken, Jan Peng, Tingying Tech Univ Munich Munich Germany Georg August Univ Goettingen Gottingen Germany Johns Hopkins Univ Baltimore MD USA Helmholtz Munich German Res Ctr Environm Hlth Inst AI Hlth Neuherberg Germany Helmholtz Munich German Res Ctr Environm Hlth Helmholtz AI Neuherberg Germany

ISBN: (纸本)9783031439926;9783031439933

Light-sheet fluorescence microscopy (LSFM), a planar illumination technique that enables high-resolution imaging of samples, experiences "defocused" image quality caused by light scattering when photons propagate through thick tissues. To circumvent this issue, dual-view imaging is helpful. It allows various sections of the specimen to be scanned ideally by viewing the sample from opposing orientations. Recent image fusion approaches can then be applied to determine infocus pixels by comparing image qualities of two views locally and thus yield spatially inconsistent focus measures due to their limited field-ofview. Here, we propose BigFUSE, a global context-aware image fuser that stabilizes image fusion in LSFM by considering the global impact of photon propagation in the specimen while determining focus-defocus based on local image qualities. Inspired by the image formation prior in dual-view LSFM, image fusion is considered as estimating a focus-defocus boundary using Bayes' theorem, where (i) the effect of light scattering onto focus measures is included within Likelihood;and (ii) the spatial consistency regarding focus-defocus is imposed in Prior. the expectation-maximum algorithm is then adopted to estimate the focus-defocus boundary. Competitive experimental results show that BigFUSE is the first dual-view LSFM fuser that is able to exclude structured artifacts when fusing information, highlighting its abilities of automatic image fusion.

关键词： Light-sheet Fluorescence Microscopy (LSFM) Multi-View image Fusion Bayesian

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Arithmetic Optimization Algorithm assisted Deep Learning Model for Remote Sensing image Classification 5

Arithmetic Optimization Algorithm Assisted Deep Learning Mod...

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5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Josephine Anitha, A. Gladis, D. University of Madras Pg and Research Department of Computer Science Chennai600 005 India Bharathi Women's College Chennai600 108 India

ISBN: (纸本)9798350321425

the spatial resolution of remote sensing images (RSI) was continuously enhanced with the advances in RSI technology. As a fundamental unit of RSI interpretation, the scene is an integration of semantics, multiple objects, and environments. RSI scene understanding not just needs to identify all objects, but needs to observe the topology distribution of many objects in an RSI scene. As a standard representative of deep learning (DL), convolutional neural networks (CNNs) are a promising method to abstract the visual contents of RSI scenes. As a result, CNN was broadly implemented in scene-driven object detection, RSI scene classification, RSI retrieval, etc. this study presents an Arithmetic Optimization Algorithm assisted Deep Learning Model for Remote Sensing image Classification (AOADL-RSIC) approach. the presented AOADL-RSIC system exploits Gaussian filtering (GF) approach to eliminate noise. In the presented AOADL-RSIC technique, SqueezeNet model is used for feature extraction process. For the classification of different objects or classes in the RSIs, extreme learning machine (ELM) model is used. Finally, the AOA is applied for the optimum parameter adjustment of the ELM approach. the simulation results of the AOADL-RSIC system are tested on EUROSAT database and the outcome displayed the significance of the AOADL-RSIC approach on image classification process. © 2023 IEEE.

关键词： Remote sensing

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Self-supervised Learning via Inter-modal Reconstruction and Feature Projection Networks for Label-Efficient 3D-to-2D Segmentation 26th

Self-supervised Learning via Inter-modal Reconstruction and ...

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

作者： Morano, Jose Aresta, Guilherme Lachinov, Dmitrii Mai, Julia Schmidt-Erfurth, Ursula Bogunovic, Hrvoje Med Univ Vienna Dept Ophthalmol & Optometry Christian Doppler Lab Artificial Intelligence Ret Vienna Austria Med Univ Vienna Dept Ophthalmol & Optometry Lab Ophthalm Image Anal Vienna Austria

ISBN: (纸本)9783031439001;9783031439018

Deep learning has become a valuable tool for the automation of certain medical image segmentation tasks, significantly relieving the workload of medical specialists. Some of these tasks require segmentation to be performed on a subset of the input dimensions, the most common case being 3D -> 2D. However, the performance of existing methods is strongly conditioned by the amount of labeled data available, as there is currently no data efficient method, e.g. transfer learning, that has been validated on these tasks. In this work, we propose a novel convolutional neural network (CNN) and self-supervised learning (SSL) method for label-efficient 3D -> 2D segmentation. the CNN is composed of a 3D encoder and a 2D decoder connected by novel 3D -> 2D blocks. the SSL method consists of reconstructing image pairs of modalities with different dimensionality. the approach has been validated in two tasks with clinical relevance: the en-face segmentation of geographic atrophy and reticular pseudodrusen in optical coherence tomography. Results on different datasets demonstrate that the proposed CNN significantly improves the state of the art in scenarios with limited labeled data by up to 8% in Dice score. Moreover, the proposed SSL method allows further improvement of this performance by up to 23%, and we show that the SSL is beneficial regardless of the network architecture.

关键词： image segmentation self-supervised learning OCT retina

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Do We Really Need that Skip-Connection? Understanding Its Interplay with Task Complexity 26th

Do We Really Need that Skip-Connection? Understanding Its In...

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

作者： Kamath, Amith Willmann, Jonas Andratschke, Nicolaus Reyes, Mauricio Univ Bern ARTORG Ctr Biomed Engn Res Bern Switzerland Univ Zurich Univ Zurich Hosp Dept Radiat Oncol Zurich Switzerland Paul Scherrer Inst Ctr Proton Therapy Villigen Switzerland Bern Univ Hosp Dept Radiat Oncol Inselspital Bern Switzerland Univ Bern Bern Switzerland

ISBN: (纸本)9783031439001;9783031439018

the U-Net architecture has become the preferred model used for medical image segmentation tasks. Since its inception, several variants have been proposed. An important component of the U-Net architecture is the use of skip-connections, said to carry over image details on its decoder branch at different scales. However, beyond this intuition, not much is known as to what extent skip-connections of the U-Net are necessary, nor what their interplay is in terms of model robustness when they are subjected to different levels of task complexity. In this study, we analyzed these questions using three variants of the U-Net architecture (the standard U-Net, a "No-Skip" U-Net, and an Attention-Gated U-Net) using controlled experiments on varying synthetic texture images and evaluated these findings on three medical image data sets. We measured task complexity as a function of texture-based similarities between foreground and background distributions. Using this scheme, our findings suggest that the benefit of employing skip-connections is small for low-to-medium complexity tasks, and its benefit appears only when the task complexity becomes large. We report that such incremental benefit is non-linear, with the Attention-Gated U-Net yielding larger improvements. Furthermore, we find that these benefits also bring along robustness degradations on clinical data sets, particularly in out-of-domain scenarios. these results suggest a dependency between task complexity and the choice/design of noise-resilient skip-connections, indicating the need for careful consideration while using these skip-connections.

关键词： image segmentation U-Net robustness

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Transferability-Guided Multi-source Model Adaptation for medical image Segmentation 26th

Transferability-Guided Multi-source Model Adaptation for Med...

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

作者： Yang, Chen Liu, Yifan Yuan, Yixuan City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Chinese Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

ISBN: (纸本)9783031438943;9783031438950

Unsupervised domain adaptation has drawn sustained attentions in medical image segmentation by transferring knowledge from labeled source data to unlabeled target domain. However, most existing approaches assume the source data are collected from a single client, which cannot be successfully applied to explore complementary transferable knowledge from multiple source domains with large distribution discrepancy. Moreover, they require access to source data during training, which is inefficient and unpractical due to privacy preservation and memory storage. To address these challenges, we study a novel and practical problem, named multi-source model adaptation (MSMA), which aims to transfer multiple source models to the unlabeled target domain without any source data. Since no target label and source data is provided to evaluate the transferability of each source model or domain gap between the source and the target domain, we may encounter negative transfer by those less related source domains, thus hurting target performance. To solve this problem, we propose a transferability-guided model adaptation (TGMA) framework to eliminate negative transfer. Specifically, 1) A label-free transferability metric (LFTM) is designed to evaluate transferability of source models without target annotations for the first time. 2) Based on the designed metric, we compute instance-level transferability matrix (ITM) for target pseudo label correction and domain-level transferability matrix (DTM) to achieve model selection for better target model initialization. Extensive experiments on multi-site prostate segmentation dataset demonstrate the superiority of our framework.

关键词： Source-free Domain Adaptation Multi-source Label-free transferability metric

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