標(biāo)題: Titlebook: Deep Learning and Data Labeling for Medical Applications; First International Gustavo Carneiro,Diana Mateus,Julien Cornebise Conference pr [打印本頁] 作者: interleukins 時(shí)間: 2025-3-21 19:00
書目名稱Deep Learning and Data Labeling for Medical Applications影響因子(影響力)
書目名稱Deep Learning and Data Labeling for Medical Applications影響因子(影響力)學(xué)科排名
書目名稱Deep Learning and Data Labeling for Medical Applications網(wǎng)絡(luò)公開度
書目名稱Deep Learning and Data Labeling for Medical Applications網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Deep Learning and Data Labeling for Medical Applications被引頻次
書目名稱Deep Learning and Data Labeling for Medical Applications被引頻次學(xué)科排名
書目名稱Deep Learning and Data Labeling for Medical Applications年度引用
書目名稱Deep Learning and Data Labeling for Medical Applications年度引用學(xué)科排名
書目名稱Deep Learning and Data Labeling for Medical Applications讀者反饋
書目名稱Deep Learning and Data Labeling for Medical Applications讀者反饋學(xué)科排名
作者: 灌溉 時(shí)間: 2025-3-21 21:11 作者: 我正派 時(shí)間: 2025-3-22 02:12 作者: 遺傳學(xué) 時(shí)間: 2025-3-22 06:37
0302-9743 n Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DL作者: 輕推 時(shí)間: 2025-3-22 10:33
Roundtable: A Discussion About Design/Repairaluations. Object search evidence obtained from three orientations and different learning architectures is consolidated through fusion schemes to lead to the target organ location. Experiments conducted using 499 patient CT body scans show promise and robustness of the proposed approach.作者: 顯微鏡 時(shí)間: 2025-3-22 15:14
https://doi.org/10.1007/978-3-031-01598-4ss validation was used giving an average accuracy of 94.5?%, a major improvement from previous methods which had an accuracy of 84?% on the same dataset. The method was also validated on a dataset of the carotid artery to show that the method can generalize to blood vessels on other regions of the body. The accuracy on this dataset was 96?%.作者: 顯微鏡 時(shí)間: 2025-3-22 20:31
Designed Technologies for Healthy Agingss longitudinal data, a novel contribution in the domain of MS lesion analysis. The method was tested on the ISBI 2015 dataset and obtained state-of-the-art Dice results with the performance level of a trained human rater.作者: Aggregate 時(shí)間: 2025-3-22 23:54 作者: INCUR 時(shí)間: 2025-3-23 01:39
Vincent Jeanne,Maarten Bodlaendereriority of the FCN over all other methods tested. Using our fully automatic algorithm we achieved true positive rate of 0.86 and 0.6 false positive per case which are very promising and clinically relevant results.作者: 標(biāo)準(zhǔn) 時(shí)間: 2025-3-23 07:37 作者: 割公牛膨脹 時(shí)間: 2025-3-23 12:08
Robust 3D Organ Localization with Dual Learning Architectures and Fusionaluations. Object search evidence obtained from three orientations and different learning architectures is consolidated through fusion schemes to lead to the target organ location. Experiments conducted using 499 patient CT body scans show promise and robustness of the proposed approach.作者: Melodrama 時(shí)間: 2025-3-23 16:26 作者: 結(jié)果 時(shí)間: 2025-3-23 19:57
Longitudinal Multiple Sclerosis Lesion Segmentation Using Multi-view Convolutional Neural Networksss longitudinal data, a novel contribution in the domain of MS lesion analysis. The method was tested on the ISBI 2015 dataset and obtained state-of-the-art Dice results with the performance level of a trained human rater.作者: 木訥 時(shí)間: 2025-3-23 23:34 作者: remission 時(shí)間: 2025-3-24 04:14
Fully Convolutional Network for Liver Segmentation and Lesions Detectioneriority of the FCN over all other methods tested. Using our fully automatic algorithm we achieved true positive rate of 0.86 and 0.6 false positive per case which are very promising and clinically relevant results.作者: Urea508 時(shí)間: 2025-3-24 07:51 作者: EXULT 時(shí)間: 2025-3-24 13:07
Designed Technologies for Healthy Aging segmentation and tracking. We evaluate our method on datasets from histology, fluorescence and phase contrast microscopy and show that it outperforms state of the art cell detection and segmentation methods.作者: antidote 時(shí)間: 2025-3-24 15:50
https://doi.org/10.1007/978-3-031-01598-4ap each input 3T patch to the 7T-like image patch. Our performance is evaluated on 15 subjects, each with both 3T and 7T MR images. Both visual and numerical results show that our method outperforms the comparison methods.作者: 抱負(fù) 時(shí)間: 2025-3-24 22:40
https://doi.org/10.1007/978-1-4471-1268-6 to segment the image into relevant landmarks, and define a set of post-processing rules to translate the segmentations into Graf’s metrics. Comparing our pipeline to estimates made by experts in DDH diagnosis shows promising results.作者: 詩集 時(shí)間: 2025-3-25 01:33 作者: BRAWL 時(shí)間: 2025-3-25 06:26 作者: 制造 時(shí)間: 2025-3-25 10:46 作者: STEER 時(shí)間: 2025-3-25 11:39
Conference proceedings 2016ge reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques..作者: Range-Of-Motion 時(shí)間: 2025-3-25 19:30
0302-9743 medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques..978-3-319-46975-1978-3-319-46976-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 摻假 時(shí)間: 2025-3-25 21:15
HEp-2 Cell Classification Using K-Support Spatial Pooling in Deep CNNserior performance compared with mean pooling strategy in the traditional state-of-the-art coding methods such as sparse coding, linear locality-constrained coding and so on. However, the max pooling strategy in SPP-net only retains the strongest activated pattern, and would completely ignore the fre作者: obeisance 時(shí)間: 2025-3-26 03:49
Fast Predictive Image Registrationetwork, which allows evaluation of deformation field uncertainty through Monte Carlo sampling using dropout at test time. We show that deformation uncertainty highlights areas of ambiguous deformations. We test our method on the OASIS brain image dataset in 2D and 3D.作者: 為寵愛 時(shí)間: 2025-3-26 06:52 作者: 阻撓 時(shí)間: 2025-3-26 08:42
De-noising of Contrast-Enhanced MRI Sequences by an Ensemble of Expert Deep Neural Networksep Neural Networks (DNNs) to address the DCE-MRI de-noising challenges. The key idea is based on an ensembling of expert DNNs, where each is trained for different noise characteristics and curve prototypes to solve an inverse problem on a specific subset of the input space. The most likely reconstru作者: 免除責(zé)任 時(shí)間: 2025-3-26 13:40
Three-Dimensional CT Image Segmentation by Combining 2D Fully Convolutional Network with 3D Majorityt any adjustment. We applied the proposed method to segment a wide range of anatomical structures that consisted of 19 types of targets in the human torso, including all the major organs. A database consisting of 240 3D CT scans and a humanly annotated ground truth was used for training and testing.作者: 同步左右 時(shí)間: 2025-3-26 18:52
Medical Image Description Using Multi-task-loss CNNates the need for hand-crafted features, and allows application of the method to new modalities and organs with minimal overhead. The proposed approach generates medical report by estimating standard radiological lexicon descriptors which are a basis for diagnosis. The proposed approach should help 作者: MAG 時(shí)間: 2025-3-26 21:13 作者: Grating 時(shí)間: 2025-3-27 01:12 作者: 沙文主義 時(shí)間: 2025-3-27 06:47 作者: condemn 時(shí)間: 2025-3-27 10:09
Eleni Kalantidou,Guy Keulemans,Alison Gillerior performance compared with mean pooling strategy in the traditional state-of-the-art coding methods such as sparse coding, linear locality-constrained coding and so on. However, the max pooling strategy in SPP-net only retains the strongest activated pattern, and would completely ignore the fre作者: LAVE 時(shí)間: 2025-3-27 17:29 作者: 食品室 時(shí)間: 2025-3-27 18:15
Designed Technologies for Healthy Aging is small relative to the dimensionality of the images. To cope with sparse voxel data, we propose utilizing the Euclidean distance transform (EDT) for increasing information density by populating each voxel with a distance value. To reduce the risk of overfitting resulting from high image dimension作者: 吞噬 時(shí)間: 2025-3-27 23:31
Vincent Jeanne,Maarten Bodlaenderep Neural Networks (DNNs) to address the DCE-MRI de-noising challenges. The key idea is based on an ensembling of expert DNNs, where each is trained for different noise characteristics and curve prototypes to solve an inverse problem on a specific subset of the input space. The most likely reconstru作者: Hyperlipidemia 時(shí)間: 2025-3-28 03:42 作者: CLOT 時(shí)間: 2025-3-28 08:16 作者: 高度贊揚(yáng) 時(shí)間: 2025-3-28 11:46 作者: OUTRE 時(shí)間: 2025-3-28 17:00 作者: 常到 時(shí)間: 2025-3-28 22:24
Deep Learning and Data Labeling for Medical Applications978-3-319-46976-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: –DOX 時(shí)間: 2025-3-29 01:35 作者: 陳舊 時(shí)間: 2025-3-29 04:49 作者: 逃避現(xiàn)實(shí) 時(shí)間: 2025-3-29 09:24 作者: WAG 時(shí)間: 2025-3-29 14:08
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/264592.jpg作者: 不自然 時(shí)間: 2025-3-29 17:37 作者: Engaged 時(shí)間: 2025-3-29 23:42
Roundtable: A Discussion About Design/Repairrch space is decomposed into two components: slice and pixel, both are modeled in 2D space. For each component, we adopt different learning architectures to leverage respective modeling power on global and local context at three orthogonal orientations. Unlike conventional patch-based scanning schem作者: 我邪惡 時(shí)間: 2025-3-30 01:58 作者: 圓木可阻礙 時(shí)間: 2025-3-30 06:14 作者: 野蠻 時(shí)間: 2025-3-30 11:45
https://doi.org/10.1007/978-3-031-01598-4ing early diagnosis of brain diseases. However, 7T MRI scanners are less accessible, compared to the 3T MRI scanners. This motivates us to reconstruct 7T-like images from 3T MRI. We propose a deep architecture for Convolutional Neural Network (CNN), which uses the . (intensity) and . (labels of brai作者: JEER 時(shí)間: 2025-3-30 15:09
Designed Technologies for Healthy Agingwork which learns the pixel/voxel-wise mapping between image appearance and registration parameters. Our approach can predict general deformation parameterizations, however, we focus on the large deformation diffeomorphic metric mapping (LDDMM) registration model. By predicting the LDDMM momentum-pa作者: 共同生活 時(shí)間: 2025-3-30 16:31 作者: ARC 時(shí)間: 2025-3-31 00:35
https://doi.org/10.1007/978-3-031-01598-4dness. Problematically, the visual indicators of ROP are not well understood and neonatal fundus images are usually of poor quality and resolution. We investigate two ways to aid clinicians in ROP detection using convolutional neural networks (CNN): (1) We fine-tune a pretrained GoogLeNet as a ROP d作者: Incommensurate 時(shí)間: 2025-3-31 03:38
Vincent Jeanne,Maarten BodlaenderCT) examinations. FCN has proven to be a very powerful tool for semantic segmentation. We explore the FCN performance on a relatively small dataset and compare it to patch based CNN and sparsity based classification schemes. Our data contains CT examinations from 20 patients with overall 68 lesions 作者: Flawless 時(shí)間: 2025-3-31 07:11 作者: CHASE 時(shí)間: 2025-3-31 10:18 作者: insomnia 時(shí)間: 2025-3-31 16:33 作者: infatuation 時(shí)間: 2025-3-31 18:54
Theoretical Approach in Design Methodologyent a new multi-task convolutional neural network (CNN) approach for detection and semantic description of lesions in diagnostic images. The proposed CNN-based architecture is trained to generate and rank rectangular regions of interests (ROI’s) surrounding suspicious areas. The highest score candid作者: 制定法律 時(shí)間: 2025-3-31 22:16 作者: 債務(wù) 時(shí)間: 2025-4-1 04:01 作者: Silent-Ischemia 時(shí)間: 2025-4-1 06:12 作者: 水汽 時(shí)間: 2025-4-1 13:42
Methoden und Dimensionen der Feldforschungfies a slice of interest (SOI) in 3D images with a convolutional neural network (ConvNet) regressor..In 150 chest CT scans two reference slices were manually identified: one containing the aortic root and another superior to the aortic arch. In two independent experiments, the ConvNet regressor was
