听力与言语-语言病理学

行为科学

医学伦理学

你正在浏览MEDICAL IMAGE ANALYSIS期刊下所有文献
  • Dynamic MRI reconstruction with end-to-end motion-guided network.

    abstract::Temporal correlation in dynamic magnetic resonance imaging (MRI), such as cardiac MRI, is informative and important to understand motion mechanisms of body regions. Modeling such information into the MRI reconstruction process produces temporally coherent image sequence and reduces imaging artifacts and blurring. Howe...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101901

    authors: Huang Q,Xian Y,Yang D,Qu H,Yi J,Wu P,Metaxas DN

    更新日期:2021-02-01 00:00:00

  • Automated size-specific dose estimates using deep learning image processing.

    abstract::An automated vendor-independent system for dose monitoring in computed tomography (CT) medical examinations involving ionizing radiation is presented in this paper. The system provides precise size-specific dose estimates (SSDE) following the American Association of Physicists in Medicine regulations. Our dose managem...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101898

    authors: Juszczyk J,Badura P,Czajkowska J,Wijata A,Andrzejewski J,Bozek P,Smolinski M,Biesok M,Sage A,Rudzki M,Wieclawek W

    更新日期:2021-02-01 00:00:00

  • HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images.

    abstract::We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional neural networks. Concentric patches at multiple resolutions with different fields of view, feed different branches of HookNet, and intermedi...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101890

    authors: van Rijthoven M,Balkenhol M,Siliņa K,van der Laak J,Ciompi F

    更新日期:2021-02-01 00:00:00

  • Gaussianization of Diffusion MRI Data Using Spatially Adaptive Filtering.

    abstract::Diffusion MRI magnitude data, typically Rician or noncentral χ distributed, is affected by the noise floor, which falsely elevates signal, reduces image contrast, and biases estimation of diffusion parameters. Noise floor can be avoided by extracting real-valued Gaussian-distributed data from complex diffusion-weighte...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101828

    authors: Liu F,Feng J,Chen G,Shen D,Yap PT

    更新日期:2021-02-01 00:00:00

  • A review of machine learning methods for retinal blood vessel segmentation and artery/vein classification.

    abstract::The eye affords a unique opportunity to inspect a rich part of the human microvasculature non-invasively via retinal imaging. Retinal blood vessel segmentation and classification are prime steps for the diagnosis and risk assessment of microvascular and systemic diseases. A high volume of techniques based on deep lear...

    journal_title:Medical image analysis

    pub_type: 杂志文章,评审

    doi:10.1016/j.media.2020.101905

    authors: Mookiah MRK,Hogg S,MacGillivray TJ,Prathiba V,Pradeepa R,Mohan V,Anjana RM,Doney AS,Palmer CNA,Trucco E

    更新日期:2021-02-01 00:00:00

  • Incomplete multi-modal representation learning for Alzheimer's disease diagnosis.

    abstract::Alzheimers disease (AD) is a complex neurodegenerative disease. Its early diagnosis and treatment have been a major concern of researchers. Currently, the multi-modality data representation learning of this disease is gradually becoming an emerging research field, attracting widespread attention. However, in practice,...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101953

    authors: Liu Y,Fan L,Zhang C,Zhou T,Xiao Z,Geng L,Shen D

    更新日期:2021-01-01 00:00:00

  • Sequential conditional reinforcement learning for simultaneous vertebral body detection and segmentation with modeling the spine anatomy.

    abstract::Accurate vertebral body (VB) detection and segmentation are critical for spine disease identification and diagnosis. Existing automatic VB detection and segmentation methods may cause false-positive results to the background tissue or inaccurate results to the desirable VB. Because they usually cannot take both the gl...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101861

    authors: Zhang D,Chen B,Li S

    更新日期:2021-01-01 00:00:00

  • Computer aided diagnosis of thyroid nodules based on the devised small-datasets multi-view ensemble learning.

    abstract::With the development of deep learning, its application in diagnosis of benign and malignant thyroid nodules has been widely concerned. However, it is difficult to obtain medical images, resulting in insufficient number of data, which contradicts the large amount of data required for acquiring effective deep learning d...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101819

    authors: Chen Y,Li D,Zhang X,Jin J,Shen Y

    更新日期:2021-01-01 00:00:00

  • A novel approach to 2D/3D registration of X-ray images using Grangeat's relation.

    abstract::Fast and accurate 2D/3D registration plays an important role in many applications, ranging from scientific and engineering domains all the way to medical care. Today's predominant methods are based on computationally expensive approaches, such as virtual forward or back projections, that limit the real-time applicabil...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101815

    authors: Frysch R,Pfeiffer T,Rose G

    更新日期:2021-01-01 00:00:00

  • Super-Resolved q-Space deep learning with uncertainty quantification.

    abstract::Diffusion magnetic resonance imaging (dMRI) provides a noninvasive method for measuring brain tissue microstructure. q-Space deep learning(q-DL) methods have been developed to accurately estimate tissue microstructure from dMRI scans acquired with a reduced number of diffusion gradients. In these methods, deep network...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101885

    authors: Qin Y,Liu Z,Liu C,Li Y,Zeng X,Ye C

    更新日期:2021-01-01 00:00:00

  • Recovering from missing data in population imaging - Cardiac MR image imputation via conditional generative adversarial nets.

    abstract::Accurate ventricular volume measurements are the primary indicators of normal/abnor- mal cardiac function and are dependent on the Cardiac Magnetic Resonance (CMR) volumes being complete. However, missing or unusable slices owing to the presence of image artefacts such as respiratory or motion ghosting, aliasing, ring...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101812

    authors: Xia Y,Zhang L,Ravikumar N,Attar R,Piechnik SK,Neubauer S,Petersen SE,Frangi AF

    更新日期:2021-01-01 00:00:00

  • Reducing annotation effort in digital pathology: A Co-Representation learning framework for classification tasks.

    abstract::Classification of digital pathology images is imperative in cancer diagnosis and prognosis. Recent advancements in deep learning and computer vision have greatly benefited the pathology workflow by developing automated solutions for classification tasks. However, the cost and time for acquiring high quality task-speci...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101859

    authors: Pati P,Foncubierta-Rodríguez A,Goksel O,Gabrani M

    更新日期:2021-01-01 00:00:00

  • Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review.

    abstract::We performed a systematic review of studies focusing on the automatic prediction of the progression of mild cognitive impairment to Alzheimer's disease (AD) dementia, and a quantitative analysis of the methodological choices impacting performance. This review included 172 articles, from which 234 experiments were extr...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101848

    authors: Ansart M,Epelbaum S,Bassignana G,Bône A,Bottani S,Cattai T,Couronné R,Faouzi J,Koval I,Louis M,Thibeau-Sutre E,Wen J,Wild A,Burgos N,Dormont D,Colliot O,Durrleman S

    更新日期:2021-01-01 00:00:00

  • CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation.

    abstract::Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model proper...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101950

    authors: Kavur AE,Gezer NS,Barış M,Aslan S,Conze PH,Groza V,Pham DD,Chatterjee S,Ernst P,Özkan S,Baydar B,Lachinov D,Han S,Pauli J,Isensee F,Perkonigg M,Sathish R,Rajan R,Sheet D,Dovletov G,Speck O,Nürnberger A,Maier-H

    更新日期:2020-12-25 00:00:00

  • Group-level cortical surface parcellation with sulcal pits labeling.

    abstract::Sulcal pits are the points of maximal depth within the folds of the cortical surface. These shape descriptors give a unique opportunity to access to a rich, fine-scale representation of the geometry and the developmental milestones of the cortical surface. However, using sulcal pits analysis at group level requires ne...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101749

    authors: Kaltenmark I,Deruelle C,Brun L,Lefèvre J,Coulon O,Auzias G

    更新日期:2020-12-01 00:00:00

  • Analytical and fast Fiber Orientation Distribution reconstruction in 3D-Polarized Light Imaging.

    abstract::Three dimensional Polarized Light Imaging (3D-PLI) is an optical technique which allows mapping the spatial fiber architecture of fibrous postmortem tissues, at sub-millimeter resolutions. Here, we propose an analytical and fast approach to compute the fiber orientation distribution (FOD) from high-resolution vector d...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101760

    authors: Alimi A,Deslauriers-Gauthier S,Matuschke F,Müller A,Muenzing SEA,Axer M,Deriche R

    更新日期:2020-10-01 00:00:00

  • Expert-validated estimation of diagnostic uncertainty for deep neural networks in diabetic retinopathy detection.

    abstract::Deep learning-based systems can achieve a diagnostic performance comparable to physicians in a variety of medical use cases including the diagnosis of diabetic retinopathy. To be useful in clinical practice, it is necessary to have well calibrated measures of the uncertainty with which these systems report their decis...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101724

    authors: Ayhan MS,Kühlewein L,Aliyeva G,Inhoffen W,Ziemssen F,Berens P

    更新日期:2020-08-01 00:00:00

  • Groupwise registration with global-local graph shrinkage in atlas construction.

    abstract::Graph-based groupwise registration methods are widely used in atlas construction. Given a group of images, a graph is built whose nodes represent the images, and whose edges represent a geodesic path between two nodes. The distribution of images on an image manifold is explored through edge traversal in a graph. The f...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101711

    authors: Fu T,Yang J,Li Q,Ai D,Song H,Jiang Y,Wang Y,Frangi AF

    更新日期:2020-08-01 00:00:00

  • Pseudo-healthy synthesis with pathology disentanglement and adversarial learning.

    abstract::Pseudo-healthy synthesis is the task of creating a subject-specific 'healthy' image from a pathological one. Such images can be helpful in tasks such as anomaly detection and understanding changes induced by pathology and disease. In this paper, we present a model that is encouraged to disentangle the information of p...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101719

    authors: Xia T,Chartsias A,Tsaftaris SA

    更新日期:2020-08-01 00:00:00

  • Dynamically constructed network with error correction for accurate ventricle volume estimation.

    abstract::Automated ventricle volume estimation (AVVE) on cardiac magnetic resonance (CMR) images is very important for clinical cardiac disease diagnosis. However, current AVVE methods ignore the error correction for the estimated volume. This results in clinically intolerable ventricle volume estimation error and further lead...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101723

    authors: Luo G,Wang W,Tam C,Wang K,Cao S,Zhang H,Chen B,Li S

    更新日期:2020-08-01 00:00:00

  • Rubik's Cube+: A self-supervised feature learning framework for 3D medical image analysis.

    abstract::Due to the development of deep learning, an increasing number of research works have been proposed to establish automated analysis systems for 3D volumetric medical data to improve the quality of patient care. However, it is challenging to obtain a large number of annotated 3D medical data needed to train a neural net...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101746

    authors: Zhu J,Li Y,Hu Y,Ma K,Zhou SK,Zheng Y

    更新日期:2020-08-01 00:00:00

  • Identifying Cross-individual Correspondences of 3-hinge Gyri.

    abstract::Human brain alignment based on imaging data has long been an intriguing research topic. One of the challenges is the huge inter-individual variabilities, which are pronounced not only in cortical folding patterns, but also in the underlying structural and functional patterns. Also, it is still not fully understood how...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101700

    authors: Zhang T,Huang Y,Zhao L,He Z,Jiang X,Guo L,Hu X,Liu T

    更新日期:2020-07-01 00:00:00

  • SDAE-GAN: Enable high-dimensional pathological images in liver cancer survival prediction with a policy gradient based data augmentation method.

    abstract::High-dimensional pathological images produced by Immunohistochemistry (IHC) methods consist of many pathological indexes, which play critical roles in cancer treatment planning. However, these indexes currently cannot be utilized in survival prediction because joining them with patients' clinicopathological features (...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101640

    authors: Wu H,Gao R,Sheng YP,Chen B,Li S

    更新日期:2020-05-01 00:00:00

  • An improved deep network for tissue microstructure estimation with uncertainty quantification.

    abstract::Deep learning based methods have improved the estimation of tissue microstructure from diffusion magnetic resonance imaging (dMRI) scans acquired with a reduced number of diffusion gradients. These methods learn the mapping from diffusion signals in a voxel or patch to tissue microstructure measures. In particular, it...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2020.101650

    authors: Ye C,Li Y,Zeng X

    更新日期:2020-04-01 00:00:00

  • LinSEM: Linearizing segmentation evaluation metrics for medical images.

    abstract::Numerous algorithms are available for segmenting medical images. Empirical discrepancy metrics are commonly used in measuring the similarity or difference between segmentations by algorithms and "true" segmentations. However, one issue with the commonly used metrics is that the same metric value often represents diffe...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2019.101601

    authors: Li J,Udupa JK,Tong Y,Wang L,Torigian DA

    更新日期:2020-02-01 00:00:00

  • Major depressive disorder identification by referenced multiset canonical correlation analysis with clinical scores.

    abstract::A novel method based on multiset canonical correlation analysis (mCCA) and linear discriminant analysis (LDA) is presented to identify the major depressive disorder (MDD). The new method comprises two parts, namely, the mCCA-rreg and sparse LDA models. The mCCA-rreg model extends the classical canonical correlation mo...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2019.101600

    authors: Lin W,Lv D,Han Z,Dong J,Yang L

    更新日期:2020-02-01 00:00:00

  • Non-invasive estimation of relative pressure in turbulent flow using virtual work-energy.

    abstract::Vascular pressure differences are established risk markers for a number of cardiovascular diseases. Relative pressures are, however, often driven by turbulence-induced flow fluctuations, where conventional non-invasive methods may yield inaccurate results. Recently, we proposed a novel method for non-turbulent flows, ...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2019.101627

    authors: Marlevi D,Ha H,Dillon-Murphy D,Fernandes JF,Fovargue D,Colarieti-Tosti M,Larsson M,Lamata P,Figueroa CA,Ebbers T,Nordsletten DA

    更新日期:2020-02-01 00:00:00

  • Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: Application to epilepsy lesion screening.

    abstract::In this study, we propose a novel anomaly detection model targeting subtle brain lesions in multiparametric MRI. To compensate for the lack of annotated data adequately sampling the heterogeneity of such pathologies, we cast this problem as an outlier detection problem and introduce a novel configuration of unsupervis...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2019.101618

    authors: Alaverdyan Z,Jung J,Bouet R,Lartizien C

    更新日期:2020-02-01 00:00:00

  • Semi-supervised mp-MRI data synthesis with StitchLayer and auxiliary distance maximization.

    abstract::The availability of a large amount of annotated data is critical for many medical image analysis applications, in particular for those relying on deep learning methods which are known to be data-hungry. However, annotated medical data, especially multimodal data, is often scarce and costly to obtain. In this paper, we...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2019.101565

    authors: Wang Z,Lin Y,Cheng KT,Yang X

    更新日期:2020-01-01 00:00:00

  • IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge.

    abstract::Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid vision loss. However, implementation of DR screening programs is chal...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2019.101561

    authors: Porwal P,Pachade S,Kokare M,Deshmukh G,Son J,Bae W,Liu L,Wang J,Liu X,Gao L,Wu T,Xiao J,Wang F,Yin B,Wang Y,Danala G,He L,Choi YH,Lee YC,Jung SH,Li Z,Sui X,Wu J,Li X,Zhou T,Toth J,Baran A,Kori A,Ch

    更新日期:2020-01-01 00:00:00

  • Automated age estimation from MRI volumes of the hand.

    abstract::Highly relevant for both clinical and legal medicine applications, the established radiological methods for estimating unknown age in children and adolescents are based on visual examination of bone ossification in X-ray images of the hand. Our group has initiated the development of fully automatic age estimation meth...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2019.101538

    authors: Štern D,Payer C,Urschler M

    更新日期:2019-12-01 00:00:00

  • RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification.

    abstract::The whole slide histopathology images (WSIs) play a critical role in gastric cancer diagnosis. However, due to the large scale of WSIs and various sizes of the abnormal area, how to select informative regions and analyze them are quite challenging during the automatic diagnosis process. The multi-instance learning bas...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2019.101549

    authors: Wang S,Zhu Y,Yu L,Chen H,Lin H,Wan X,Fan X,Heng PA

    更新日期:2019-12-01 00:00:00

  • Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge.

    abstract::Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be challenging due to the l...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2019.101537

    authors: Zhuang X,Li L,Payer C,Štern D,Urschler M,Heinrich MP,Oster J,Wang C,Smedby Ö,Bian C,Yang X,Heng PA,Mortazi A,Bagci U,Yang G,Sun C,Galisot G,Ramel JY,Brouard T,Tong Q,Si W,Liao X,Zeng G,Shi Z,Zheng G,Wang

    更新日期:2019-12-01 00:00:00

  • Disentangled representation learning in cardiac image analysis.

    abstract::Typically, a medical image offers spatial information on the anatomy (and pathology) modulated by imaging specific characteristics. Many imaging modalities including Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) can be interpreted in this way. We can venture further and consider that a medical image na...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2019.101535

    authors: Chartsias A,Joyce T,Papanastasiou G,Semple S,Williams M,Newby DE,Dharmakumar R,Tsaftaris SA

    更新日期:2019-12-01 00:00:00

  • Hierarchical spherical deformation for cortical surface registration.

    abstract::We present hierarchical spherical deformation for a group-wise shape correspondence to address template selection bias and to minimize registration distortion. In this work, we aim at a continuous and smooth deformation field to guide accurate cortical surface registration. In conventional spherical registration metho...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2019.06.013

    authors: Lyu I,Kang H,Woodward ND,Styner MA,Landman BA

    更新日期:2019-10-01 00:00:00

  • A gradient-based optical-flow cardiac motion estimation method for cine and tagged MR images.

    abstract::A new method is proposed to quantify the myocardial motion from both 2D C(ine)-MRI and T(agged)-MRI sequences. The tag pattern offers natural landmarks within the image that makes it possible to accurately quantify the motion within the myocardial wall. Therefore, several methods have been proposed for T-MRI. However,...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2019.06.016

    authors: Wang L,Clarysse P,Liu Z,Gao B,Liu W,Croisille P,Delachartre P

    更新日期:2019-10-01 00:00:00

  • Suite of meshless algorithms for accurate computation of soft tissue deformation for surgical simulation.

    abstract::The ability to predict patient-specific soft tissue deformations is key for computer-integrated surgery systems and the core enabling technology for a new era of personalized medicine. Element-Free Galerkin (EFG) methods are better suited for solving soft tissue deformation problems than the finite element method (FEM...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2019.06.004

    authors: Joldes G,Bourantas G,Zwick B,Chowdhury H,Wittek A,Agrawal S,Mountris K,Hyde D,Warfield SK,Miller K

    更新日期:2019-08-01 00:00:00

  • Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow.

    abstract::We propose a method to classify cardiac pathology based on a novel approach to extract image derived features to characterize the shape and motion of the heart. An original semi-supervised learning procedure, which makes efficient use of a large amount of non-segmented images and a small amount of images segmented man...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2019.06.001

    authors: Zheng Q,Delingette H,Ayache N

    更新日期:2019-08-01 00:00:00

  • Attentive neural cell instance segmentation.

    abstract::Neural cell instance segmentation, which aims at joint detection and segmentation of every neural cell in a microscopic image, is essential to many neuroscience applications. The challenge of this task involves cell adhesion, cell distortion, unclear cell contours, low-contrast cell protrusion structures, and backgrou...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2019.05.004

    authors: Yi J,Wu P,Jiang M,Huang Q,Hoeppner DJ,Metaxas DN

    更新日期:2019-07-01 00:00:00

  • Abdominal multi-organ segmentation with organ-attention networks and statistical fusion.

    abstract::Accurate and robust segmentation of abdominal organs on CT is essential for many clinical applications such as computer-aided diagnosis and computer-aided surgery. But this task is challenging due to the weak boundaries of organs, the complexity of the background, and the variable sizes of different organs. To address...

    journal_title:Medical image analysis

    pub_type: 杂志文章

    doi:10.1016/j.media.2019.04.005

    authors: Wang Y,Zhou Y,Shen W,Park S,Fishman EK,Yuille AL

    更新日期:2019-07-01 00:00:00

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