標(biāo)題: Titlebook: Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics; Le Lu,Xiaosong Wang,Lin Yang Book 2019 Sprin [打印本頁] 作者: 生長變吼叫 時(shí)間: 2025-3-21 16:18
書目名稱Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics影響因子(影響力)
書目名稱Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics影響因子(影響力)學(xué)科排名
書目名稱Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics網(wǎng)絡(luò)公開度
書目名稱Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics被引頻次
書目名稱Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics被引頻次學(xué)科排名
書目名稱Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics年度引用
書目名稱Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics年度引用學(xué)科排名
書目名稱Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics讀者反饋
書目名稱Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics讀者反饋學(xué)科排名
作者: 變態(tài) 時(shí)間: 2025-3-21 23:14
978-3-030-13971-1Springer Nature Switzerland AG 2019作者: 玷污 時(shí)間: 2025-3-22 03:58 作者: Encumber 時(shí)間: 2025-3-22 04:34
Philipp Jordan,Paula Alexandra Silva critical to guide effective patient treatment since each subtype requires distinct therapy. Image analysis?of hematoxylin and eosin (H&E)-stained whole-slide specimens of muscle biopsies are considered as a gold standard for effective IM diagnosis. Accurate segmentation of perimysium plays an impor作者: senile-dementia 時(shí)間: 2025-3-22 09:13
https://doi.org/10.1007/978-3-030-78221-4gans?(e.g., .) or neoplasms (e.g., .) is sometimes below satisfaction, arguably because deep networks are easily disrupted by the complex and variable background regions which occupy a large fraction of the input volume. In this chapter, we propose two coarse-to-fine mechanisms which use prediction 作者: Tailor 時(shí)間: 2025-3-22 13:47
Lecture Notes in Computer Science image segmentation, due to the limited amount of annotated 3D data and limited computational resources. In this chapter, by rethinking the strategy to apply 3D Convolutional Neural Networks?to segment medical images, we propose a novel 3D-based coarse-to-fine?framework to efficiently tackle these c作者: Tailor 時(shí)間: 2025-3-22 19:31 作者: LATHE 時(shí)間: 2025-3-23 01:07 作者: 奇怪 時(shí)間: 2025-3-23 02:02 作者: SLAG 時(shí)間: 2025-3-23 07:58 作者: INCUR 時(shí)間: 2025-3-23 13:39 作者: PAD416 時(shí)間: 2025-3-23 16:29 作者: 掙扎 時(shí)間: 2025-3-23 21:43
Evaluation of Contractor’s Tender Proposalsnce to support the diagnosis. Hashing is an important tool in CBIR due to the significant gain in both computation and storage. Because of the tremendous success of deep learning, deep hashing?simultaneously learning powerful feature representations and binary codes has achieved promising performanc作者: 山羊 時(shí)間: 2025-3-23 23:58 作者: ACTIN 時(shí)間: 2025-3-24 06:12 作者: Chemotherapy 時(shí)間: 2025-3-24 07:06
https://doi.org/10.1007/0-306-48631-8ducing the radiation dose?may lead to increased noise and artifacts, which can adversely affect radiologists’ judgment and confidence. Hence, advanced image reconstruction from low-dose CT data is needed to improve the diagnostic performance, which is a challenging problem due to its ill-posed natur作者: abstemious 時(shí)間: 2025-3-24 11:25
Design-Oriented Analysis of Structuresmaging modality for noninvasive and nonionizing imaging of the heart and great vessels and, hence, most suited for population imaging cohorts. Ensuring full coverage of the left ventricle (LV)?is a basic criterion of CMR image quality. Complete LV coverage, from base to apex, precedes accurate cardi作者: Stress-Fracture 時(shí)間: 2025-3-24 18:09
Blanca Callén Moreu,Melisa Duque Hurtadoed research field. Prior to the popularity of deep learning, image registration?was commonly performed by optimizing an image matching metric as a cost function in search for the optimal registration. However, the optimization task is known to be challenging due to (1) the non-convex nature of the m作者: deforestation 時(shí)間: 2025-3-24 21:22 作者: BARK 時(shí)間: 2025-3-24 23:50 作者: 無能的人 時(shí)間: 2025-3-25 05:37 作者: 殺蟲劑 時(shí)間: 2025-3-25 11:03 作者: Enliven 時(shí)間: 2025-3-25 12:22 作者: 經(jīng)典 時(shí)間: 2025-3-25 19:14 作者: Limerick 時(shí)間: 2025-3-25 23:44
Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-Fine Framework and Its Adversarial Example last one contain healthy and pathological pancreases, respectively, and achieve the current state of the art in terms of Dice-S?rensen Coefficient (DSC) on all of them. Especially, on the NIH pancreas dataset, we outperform the previous best by an average of over ., and the worst case is improved 作者: cringe 時(shí)間: 2025-3-26 02:29
Glaucoma Detection Based on Deep Learning Network in Fundus Imageided network, local disc region stream, and disc polar transformation stream. The DENet produces the glaucoma detection?result from the image directly without segmentation. Finally, we compare two deep learning?methods with other related methods on several glaucoma detection datasets.作者: 調(diào)整 時(shí)間: 2025-3-26 08:18 作者: Aspirin 時(shí)間: 2025-3-26 10:12
Anisotropic Hybrid Network for Cross-Dimension Transferable Feature Learning in 3D Medical Imageso 3D anisotropic volumes. Such a transfer inherits the desired strong generalization capability for within-slice information while naturally exploiting between-slice information for more effective modeling. We show the effectiveness of the 3D AH-Net on two example medical image analysis?applications作者: Frequency-Range 時(shí)間: 2025-3-26 13:37
Tumor Growth Prediction Using Convolutional Networksn. We then present a two-stream ConvNets which directly model and learn the two fundamental processes of tumor growth, i.e., cell invasion and mass effect, and predict the subsequent involvement regions of a tumor. Experiments on a longitudinal?pancreatic tumor data set show that both approaches sub作者: 奇思怪想 時(shí)間: 2025-3-26 20:08 作者: idiopathic 時(shí)間: 2025-3-26 22:45
Generative Low-Dose CT Image Denoisingibility of important structural details after aggressive denoising. This paper introduces a new CT image denoising?method based on the generative adversarial network (GAN)?with Wasserstein distance and perceptual similarity. The Wasserstein distance is a key concept of the optimal transport theory, 作者: FRAX-tool 時(shí)間: 2025-3-27 04:47 作者: 實(shí)施生效 時(shí)間: 2025-3-27 05:35 作者: LIMN 時(shí)間: 2025-3-27 10:24 作者: hematuria 時(shí)間: 2025-3-27 15:34 作者: 分期付款 時(shí)間: 2025-3-27 20:42
Lecture Notes in Computer Sciencee last one contain healthy and pathological pancreases, respectively, and achieve the current state of the art in terms of Dice-S?rensen Coefficient (DSC) on all of them. Especially, on the NIH pancreas dataset, we outperform the previous best by an average of over ., and the worst case is improved 作者: expeditious 時(shí)間: 2025-3-28 01:01 作者: 輪流 時(shí)間: 2025-3-28 05:29
Yu-Yi Ding,Jing-Hua Han,Qi Cao,Chao Liu?from DI2IN within multiple iterations, according to the spatial relationship of vertebrae. Finally, the locations of vertebra are refined and constrained with a learned sparse representation. We evaluate the proposed method on two categories of public databases, 3D CT volumes, and 2D X-ray scans, u作者: 大火 時(shí)間: 2025-3-28 08:23
Wei Li,Xuan Zhang,Yi Shen Zhango 3D anisotropic volumes. Such a transfer inherits the desired strong generalization capability for within-slice information while naturally exploiting between-slice information for more effective modeling. We show the effectiveness of the 3D AH-Net on two example medical image analysis?applications作者: Keratin 時(shí)間: 2025-3-28 12:35
Evaluation of Contractor’s Tender Proposalsn. We then present a two-stream ConvNets which directly model and learn the two fundamental processes of tumor growth, i.e., cell invasion and mass effect, and predict the subsequent involvement regions of a tumor. Experiments on a longitudinal?pancreatic tumor data set show that both approaches sub作者: 擴(kuò)張 時(shí)間: 2025-3-28 18:24
https://doi.org/10.1007/978-1-349-21601-7resolution patches at different cross sections of the spatial-temporal data and reconstructs high-quality CT volumes. We assess the performance of the network concerning image restoration?at different tube currents and multiple resolution scales. The results indicate the ability of our network in re作者: NOCT 時(shí)間: 2025-3-28 19:43
https://doi.org/10.1007/0-306-48631-8ibility of important structural details after aggressive denoising. This paper introduces a new CT image denoising?method based on the generative adversarial network (GAN)?with Wasserstein distance and perceptual similarity. The Wasserstein distance is a key concept of the optimal transport theory, 作者: 調(diào)色板 時(shí)間: 2025-3-28 23:36 作者: Gossamer 時(shí)間: 2025-3-29 06:32 作者: ITCH 時(shí)間: 2025-3-29 08:19 作者: Certainty 時(shí)間: 2025-3-29 14:30 作者: Cloudburst 時(shí)間: 2025-3-29 16:00
Pancreas Segmentation in CT and MRI via Task-Specific Network Design and Recurrent Neural Contextualomputer-aided screening, diagnosis, and quantitative assessment. Yet, pancreas is a challenging abdominal organ?to segment due to the high inter-patient anatomical variability in both shape and volume metrics. Recently, convolutional neural networks?(CNN) have demonstrated promising performance on a作者: visual-cortex 時(shí)間: 2025-3-29 20:23
Deep Learning for Muscle Pathology Image Analysis critical to guide effective patient treatment since each subtype requires distinct therapy. Image analysis?of hematoxylin and eosin (H&E)-stained whole-slide specimens of muscle biopsies are considered as a gold standard for effective IM diagnosis. Accurate segmentation of perimysium plays an impor作者: Working-Memory 時(shí)間: 2025-3-30 01:41
2D-Based Coarse-to-Fine Approaches for Small Target Segmentation in Abdominal CT Scansgans?(e.g., .) or neoplasms (e.g., .) is sometimes below satisfaction, arguably because deep networks are easily disrupted by the complex and variable background regions which occupy a large fraction of the input volume. In this chapter, we propose two coarse-to-fine mechanisms which use prediction 作者: RAFF 時(shí)間: 2025-3-30 04:07 作者: Biofeedback 時(shí)間: 2025-3-30 08:43 作者: deactivate 時(shí)間: 2025-3-30 15:07
Glaucoma Detection Based on Deep Learning Network in Fundus Images based on deep learning?technique. The first is the multi-label?segmentation network, named M-Net, which solves the optic disc and optic cup segmentation jointly. M-Net contains a multi-scale U-shape convolutional network with the side-output layer to learn discriminative representations and produc作者: 擴(kuò)張 時(shí)間: 2025-3-30 16:48
Thoracic Disease Identification and Localization with Limited Supervisionilding a highly accurate prediction model for these tasks usually requires a large number of images manually annotated with labels and finding sites of abnormalities. In reality, however, such annotated data are expensive to acquire, especially the ones with location annotations. We need methods tha作者: 材料等 時(shí)間: 2025-3-30 21:13
Deep Reinforcement Learning for Detecting Breast Lesions from DCE-MRI resonance images (DCE-MRI) at state-of-the-art accuracy. In contrast to previous methods based on computationally expensive exhaustive search strategies, our method reduces the inference time with a search approach that gradually focuses on lesions by progressively transforming a bounding volume un作者: BORE 時(shí)間: 2025-3-31 02:45 作者: oblique 時(shí)間: 2025-3-31 08:11 作者: 散步 時(shí)間: 2025-3-31 10:51 作者: Indebted 時(shí)間: 2025-3-31 13:54 作者: mutineer 時(shí)間: 2025-3-31 21:11
Deep Spatial-Temporal Convolutional Neural Networks for Medical Image Restorationy and blood flow in virtually live time. However, effective visualization exposes patients to radiocontrast pharmaceuticals and extended scan times. Higher radiation dosage exposes patients to potential risks including hair loss, cataract formation, and cancer. To alleviate these risks, radiation do作者: 摘要 時(shí)間: 2025-4-1 01:22
