ライフサイエンスコーパス: convolutional neural network

1 g models (principal component analysis and a convolutional neural network).

2 ed of the psoas and iliacus muscles) using a convolutional neural network.

3 was extended to all miRNA sequences using a convolutional neural network.

4 istil the knowledge of an expert into a deep convolutional neural network.

5 ase variation and resolution when training a convolutional neural network.

6 to train, validate, and test an inception-v4 convolutional neural network.

7 gression area was done by using a supervised convolutional neural network.

8 ned and abnormality features extracted using convolutional neural networks.

9 le slide images (WSIs) to train and evaluate convolutional neural networks.

10 the field of radiologic image analysis using convolutional neural networks.

11 Both algorithms used one-dimensional convolutional neural networks.

12 ification by image-based analysis using deep convolutional neural networks.

13 ionary precision matrices with deep residual convolutional neural-networks.

14 ta is performed using a one-dimensional deep convolutional neural network (1D-CNN).

15 e trained 3 types of models on our data: (1) convolutional neural networks, (2) random forest, and (3

16 In addition, a two-dimensional convolutional neural network (2D-CNN) was developed usin

17 We introduce ThermoNet, a deep, 3D-convolutional neural network (3D-CNN) designed for struc

18 present a general framework that applies 3D convolutional neural networks (3DCNNs) to structure-base

19 The approach uses the convolutional neural network, a powerful image classific

20 Here we present a convolutional neural network, Akita, that accurately pre

21 The convolutional neural network allowed high-throughput and

22 The convolutional neural network also showed high performanc

23 We have developed and evaluated convolutional neural network analysis pipelines to gener

24 A deep learning algorithm using convolutional neural network and long short-term memory

25 ulatory code of DNA methylation using a deep convolutional neural network and uses this network to pr

26 hat uses the node2vec technique, multi-scale convolutional neural networks and a sampling technique t

27 Three-dimensional convolutional neural networks and atlas-based image proc

28 were dissociated based on representations in convolutional neural networks and behavioral experiments

29 Other models, such as those based on RNN, convolutional neural networks and CRF alone had lower pe

30 s and intervals using a novel combination of convolutional neural networks and hidden Markov models a

31 l scar volume, outperforms a two-dimensional convolutional neural network, and demonstrates comparabl

32 oherent imaging systems using image data and convolutional neural networks, and provides a rapid, non

33 ies and nuclei from whole slide images using convolutional neural networks, and the remaining glomeru

34 We investigated the efficiency of a convolutional neural network applied to corneal topograp

35 Here we present NeuSomatic, the first convolutional neural network approach for somatic mutati

36 ly, and reliably using cutting-edge regional convolutional neural network architecture (Faster-RCNN).

37 We investigated use of a convolutional neural network architecture for accurate s

38 The CapsNet outperformed the baseline convolutional neural network architecture MusiteDeep and

39 ofiles are treated as images and multi-scale convolutional neural networks are applied to extract the

40 , especially with use of deep (multilayered) convolutional neural networks, artificial intelligence h

41 and Methods For this retrospective study, a convolutional neural network based on the U-Net architec

42 To address this challenge, we crafted a convolutional neural network-based architecture to produ

43 Here we describe convolutional neural network-based automated cell classi

44 Algorithmic- and convolutional neural network-based image analysis extrac

45 extracted pharmacokinetic parameters through convolutional neural network-based image processing, inc

46 In this paper, we present EnvCNN, a convolutional neural network-based model for evaluating

47 ing data will further improve performance of convolutional neural network, but not that of radiomics

48 have developed a new approach-based on deep convolutional neural networks-called MesoNet to accurate

49 Conclusion Convolutional neural networks can identify vertebral fra

50 Convolutional neural networks can solve this generalized

51 h are then used as the input to a pretrained convolutional neural network classifier.

52 to count overlapping cells with a pretrained convolutional neural network classifier.

53 a pooled AUC of 0.91 (95% CI 0.81-0.96), and convolutional neural network (CNN) algorithms had a pool

54 , classifier results were compared against a convolutional neural network (CNN) and diagnostic readin

55 s, we constructed a pipeline equipped with a convolutional neural network (CNN) and soft-voting as th

56 e clarity, we introduce DefectSegNet - a new convolutional neural network (CNN) architecture that per

57 a feedforward deep neural network (DNN) and convolutional neural network (CNN) architecture.

58 is then used as an input to train and test a convolutional neural network (CNN) based classifier.

59 In this work, we present a convolutional neural network (CNN) based method for cone

60 Here we developed a Convolutional Neural Network (CNN) based method, called

61 The proposed end-to-end convolutional neural network (CNN) based model extracts

62 We developed three convolutional neural network (CNN) classification models

63 were used to develop 3 competing multi-class convolutional neural network (CNN) classifiers for modif

64 We hypothesized that a convolutional neural network (CNN) could be trained thro

65 y across 81 immune cell types, we asked if a convolutional neural network (CNN) could learn to infer

66 eternamoves-select, we present a multilayer convolutional neural network (CNN) EternaBrain that achi

67 fully automatic framework consisted of (a) a convolutional neural network (CNN) for localization and

68 An encoder-decoder based convolutional neural network (CNN) is designed and train

69 Here, a convolutional neural network (CNN) is proposed to enable

70 We built a convolutional neural network (CNN) model to predict brai

71 A convolutional neural network (CNN) model was trained to

72 In this work, we demonstrate that a convolutional neural network (CNN) model when applied to

73 f parasites and slide interpretation using a convolutional neural network (CNN) model.

74 he datasets thus generated are used to train convolutional neural network (CNN) models and evaluate t

75 Deep learning convolutional neural network (CNN) models have demonstra

76 Here, we evaluate the advantages of training convolutional neural network (CNN) models on a broad set

77 We therefore investigate the application of convolutional neural network (CNN) models to the discove

78 We then apply a regular 3D convolutional neural network (CNN) on the tumor segments

79 l line dataset, we demonstrate that a simple convolutional neural network (CNN) performs as well as,

80 features are extracted using the pretrained convolutional neural network (CNN) ResNet-50, and then t

81 utomated segmentation pipeline including two convolutional neural network (CNN) segmentation algorith

82 To overcome these, we trained a convolutional neural network (CNN) to develop a framewor

83 od, chi-CNN, uses this data to first train a convolutional neural network (CNN) to discriminate betwe

84 stage I - III colorectal cancer (CRC) using convolutional neural network (CNN) to elucidate its clin

85 Here, we develop a convolutional neural network (CNN) to estimate regional

86 performance of radiomic analysis (RA) and a convolutional neural network (CNN) to radiologists for c

87 ng with deep learning approaches, a 2D dense convolutional neural network (CNN) was implemented and t

88 A convolutional neural network (CNN) was trained and valid

89 A pretrained convolutional neural network (CNN) was used to extract f

90 DeepCirCode utilizes a convolutional neural network (CNN) with nucleotide seque

91 The machine-learning algorithm trains a deep convolutional neural network (CNN) with U-shaped archite

92 diction by harnessing the capability of deep convolutional neural network (CNN) within a MIL framewor

93 irectional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network (CNN), and Conditional Rand

94 Here, we evaluated the use of a deep convolutional neural network (CNN), trained using single

95 The 3D convolutional neural network (CNN)-based classification

96 We compared our convolutional neural network (CNN)-based contact predict

97 evelop and evaluate a three-dimensional (3D) convolutional neural network (CNN)-based method for auto

98 implements multiple instance learning with a convolutional neural network (CNN).

99 on of fluid with every voxel classified by a convolutional neural network (CNN).

100 implementing a Deep Learning approach called Convolutional Neural Network (CNN).

101 applying AI algorithms including a blend of Convolutional Neural Networks (CNN) and Fully Connected

102 on, random forests, gradient boosting trees, convolutional neural networks (CNN) and recurrent neural

103 Meanwhile, convolutional neural networks (CNN) are rapidly emerging

104 The current state of the art is to employ convolutional neural networks (CNN) encoded within deep-

105 A variety of solutions using convolutional neural networks (CNN) for EEG classificati

106 We hypothesized that convolutional neural networks (CNN) may enable objective

107 Plus, convolutional neural networks (CNN) provide the best-in-

108 res, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-

109 Recently developed convolutional neural networks (CNN), which are capable o

110 Non-recurrent deep convolutional neural networks (CNNs) are currently the b

111 In BCM3D, deep convolutional neural networks (CNNs) are trained using s

112 Here we show that convolutional neural networks (CNNs) can be systematical

113 Purpose To determine whether convolutional neural networks (CNNs) can be trained to i

114 present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognos

115 Deep learning techniques such as convolutional neural networks (CNNs) can potentially pro

116 e propose a multi-channel architecture of 3D convolutional neural networks (CNNs) for deep learning u

117 We investigate convolutional neural networks (CNNs) for filtering small

118 Convolutional neural networks (CNNs) have achieved human

119 Recent breakthroughs in DL, in particular, convolutional neural networks (CNNs) have achieved remar

120 In recent years, convolutional neural networks (CNNs) have become ubiquit

121 Although convolutional neural networks (CNNs) have been applied t

122 s extracted from PPG, whereas for DL several convolutional neural networks (CNNs) have been applied t

123 Convolutional Neural Networks (CNNs) have been successfu

124 In conclusion, multi-layer convolutional neural networks (CNNs) set the new state o

125 The proposed method is an ensemble of deep convolutional neural networks (CNNs) that detect more di

126 Current state-of-the-art techniques rely on convolutional neural networks (CNNs) that use pixel-to-p

127 we developed deep learning algorithms using convolutional neural networks (CNNs) to automatically se

128 This entailed employing Convolutional Neural Networks (CNNs) to construct an ech

129 Purpose To assess the ability of convolutional neural networks (CNNs) to enable high-perf

130 Purpose To evaluate configurations of deep convolutional neural networks (CNNs) to localize and cla

131 p learning model that uses a set of cascaded convolutional neural networks (CNNs) to predict Calpha a

132 a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosi

133 an be removed in whole-body bone scans using convolutional neural networks (CNNs) trained with sets o

134 Here we assessed the use of convolutional neural networks (CNNs) using free, open so

135 Convolutional neural networks (CNNs), a form of DL, were

136 ges using handcrafted (HC) features and deep convolutional neural networks (CNNs), and analyze the re

137 Machine learning techniques, such as convolutional neural networks (CNNs), are well-suited fo

138 cial intelligence (AI) system, based on deep convolutional neural networks (CNNs), for automated real

139 s fall into three classes: Some are based on convolutional neural networks (CNNs), others use recurre

140 The approach is based on convolutional neural networks (CNNs), which may be embed

141 Existing solutions are mostly based on Convolutional Neural Networks (CNNs), which require a la

142 However, convolutional neural networks (CNNs)-one of the most imp

143 ural network techniques based on one or more convolutional neural networks (CNNs).

144 , DeepTE, which classifies unknown TEs using convolutional neural networks (CNNs).

145 Convolutional neural networks (ConvNets) have proven to

146 In this work, we study how deep convolutional neural networks (ConvNets) may be best des

147 After network structure optimization, the convolutional neural network could achieve 91.13% accura

148 , a three-dimensional (3D) densely connected convolutional neural network (DCNet) is proposed to prov

149 The aim of this study was to use deep convolutional neural network (DCNN) models to improve th

150 First, we trained a deep convolutional neural network (DCNN) to map the surface e

151 The discovery that deep convolutional neural networks (DCNNs) achieve human perf

152 chical correspondence between layers of deep convolutional neural networks (DCNNs) and cascade of reg

153 mbination of two powerful technologies: deep convolutional neural networks (DCNNs) and panoramic vide

154 Deep convolutional neural networks (DCNNs) are frequently des

155 Purpose To explore the use of deep convolutional neural networks (DCNNs) to generate synthe

156 Deep convolutional neural networks (DCNNs) were trained indep

157 d to be superior to other ML methods such as convolutional neural network, decision tree, and eXtreme

158 The convolutional neural network, DeepMedic, was trained on

159 the model performance by race/ethnicity for convolutional neural network designed to identify patien

160 s and Methods DeepCOVID-XR is an ensemble of convolutional neural networks developed to detect COVID-

161 The proposed convolutional neural network enables accurate artery and

162 rdiomyopathy population, a three-dimensional convolutional neural network enables fast and accurate q

163 the LV blood pool center-point using a fully convolutional neural network (FCN) architecture.

164 We propose using faster regions with convolutional neural network features (faster R-CNN) in

165 Feedforward Convolutional Neural Networks (ffCNNs) have shown how po

166 In this study, we propose and test a deep convolutional neural network for analyzing cytometry dat

167 We show that our method, convolutional neural network for coexpression (CNNC), im

168 Here, we present a new method that employs a convolutional neural network for detecting presence of i

169 (iii) FullMeSH trains an Attention-based Convolutional Neural Network for each section, which ach

170 A deep convolutional neural network for image quality assessmen

171 We trained a convolutional neural network for multiclass segmentation

172 This study presents the first convolutional neural network for multiclass segmentation

173 proach using AtomNet, the world's first deep convolutional neural network for structure-based drug di

174 To achieve this, we developed Graph Convolutional Neural networks for Genes (GCNG).

175 Deep convolutional neural networks for images and long short-

176 of >2000 functional features, we developed a convolutional neural network framework for combinatorial

177 First, the sequence-embedding convolutional neural network generalizes the existing k-

178 diverse pulmonary pathologic findings, deep convolutional neural networks generated ventilation maps

179 ep learning model that unifies recurrent and convolutional neural networks has been proposed to explo

180 Deep learning with Convolutional Neural Networks has shown great promise in

181 Recently, convolutional neural networks have been used to learn pr

182 r biomedical image computing; in particular, convolutional neural networks have been widely applied t

183 Many machine learning algorithms, including convolutional neural networks, have been proposed to aut

184 learning machine learning methods, including convolutional neural networks, have enabled the developm

185 ECG measurements derived from the convolutional neural network-hidden Markov model segment

186 essment images with high accuracy, and these convolutional neural network-identified vertebral fractu

187 ssion to (i) levels of inflammation, using a convolutional neural network (ii) equine papillomavirus

188 Here we describe OrgaQuant, a deep convolutional neural network implementation that can loc

189 PIPR incorporates a deep residual recurrent convolutional neural network in the Siamese architecture

190 a set (area under the curve [AUC]=0.662) and convolutional neural network in the validation dataset (

191 It employs a state-of-the-art convolutional neural network in which biomolecular struc

192 ymptomatic adults by using three-dimensional convolutional neural networks, including a subcohort wit

193 combined corneal topography raw data with a convolutional neural network is an effective way to clas

194 For global sequence features, a text convolutional neural network is applied to extract featu

195 nts with lung cancer and lymphoma by using a convolutional neural network is feasible and achieves hi

196 tween vectors that represent spectra, a deep convolutional neural network is used.

197 A deep learning technique, the convolutional neural network, is increasingly applied in

198 Training an ensemble of convolutional neural networks jointly on the two data se

199 In this work, we proposed a novel deep convolutional neural network model (DCNN) for HLA-peptid

200 Port database, we demonstrated that the deep convolutional neural network model can accurately diagno

201 Third, a final 3-dimensional convolutional neural network model evaluated echocardiog

202 eloped Clairvoyante, a multi-task five-layer convolutional neural network model for predicting varian

203 First, we developed a 3-dimensional convolutional neural network model for view selection en

204 odel by integrating a new sequence-embedding convolutional neural network model over a thermodynamic

205 nt of IPNs.Methods: A Lung Cancer Prediction Convolutional Neural Network model was trained using com

206 Here, we constructed a convolutional neural network model with a large-scale GW

207 Before using the images as input to a convolutional neural network model, they were standardiz

208 ation-based method for interpreting the deep convolutional neural network model.

209 Here we show: (1) Recurrent convolutional neural network models outperform feedforwa

210 rained on object recognition and data-driven convolutional neural network models trained end-to-end o

211 IT image memorability modulation, we probed convolutional neural network models trained to categoriz

212 taset, where FDP-M-net and U-finger are both convolutional neural network models.

213 d 683 bacteria species as input to train two Convolutional Neural Network models.

214 Two different convolutional neural networks models (MobileNet and Ince

215 ng rotation equivariance and invariance in a convolutional neural network, namely, the group-equivari

216 d TIL analyses were evaluated by using three convolutional neural network networks (34-layer ResNet,

217 Convolutional neural network of InceptionV3 architecture

218 lemented a program, called ImaGene, to apply convolutional neural networks on population genomic data

219 Our proposed SinoNet, a convolutional neural network optimized for interpreting

220 A Densely Connected Convolutional Neural Network (or DenseNet) was trained o

221 Analysis powered by trained convolutional neural networks precisely identified featu

222 nd features were extracted with a pretrained convolutional neural network (preprocessing step).

223 ur cohorts were used to train a total of ten convolutional neural networks, purpose-built for classif

224 of the mandibular canals by applying a fully convolutional neural network segmentation on clinically

225 Here, we develop a strategy to train deep convolutional neural networks simultaneously on multiple

226 processing and feature extraction steps by a convolutional neural network that directly operates on t

227 e transmitted light images are passed into a convolutional neural network that then outputs predicted

228 The convolutional neural network that was previously derived

229 introduce a methodology to train ResNet-type convolutional neural networks that results in no appreci

230 Emap2sec uses a three-dimensional deep convolutional neural network to assign secondary structu

231 esign a framework based on Mask Region-based Convolutional Neural Network to automatically detect and

232 erate neuronal reconstructions, we trained a convolutional neural network to automatically segment ne

233 Purpose To develop a convolutional neural network to detect LVOs at multiphas

234 AI)-enabled electrocardiograph (ECG) using a convolutional neural network to detect the electrocardio

235 We developed DeepSAV, a deep-learning convolutional neural network to differentiate disease-ca

236 We exploited a convolutional neural network to embed soundscapes from a

237 d named HiCNN that used a 54-layer very deep convolutional neural network to enhance the resolutions

238 ions were used to train a keypoint detection convolutional neural network to find new lesions.

239 oton calcium imaging movies, we propose a 3D convolutional neural network to identify and segment act

240 Therefore, we used a deep convolutional neural network to identify fundamental dis

241 59 patients at the Mayo Clinic, we trained a convolutional neural network to identify patients with v

242 Our model uses a convolutional neural network to identify regions of neop

243 tomated stomata counting system using a deep convolutional neural network to identify stomata in a va

244 city to label-free images, we trained a deep-convolutional neural network to perform semantic segment

245 PlantSeg employs a convolutional neural network to predict cell boundaries

246 To address this challenge, we have trained a convolutional neural network to predict functional statu

247 y race, this did not impact the ability of a convolutional neural network to predict low left ventric

248 we first propose a framework that uses deep convolutional neural network to recognize the typeface f

249 This article shows how to train a convolutional neural network to reduce noise in CT image

250 oy activation patterns extracted from a deep convolutional neural network to reveal that these differ

251 We trained the convolutional neural network to segment six major renal

252 ter isotropic resolution (BigBrain), using a convolutional neural network to segment, automatically,

253 extracted from 37 patients were used in the convolutional neural network to train and as internal va

254 We used fully convolutional neural networks to automatically detect se

255 ge Recognition and Analysis' (LIRA), employs convolutional neural networks to classify seven differen

256 features from clinical and genomic data, and convolutional neural networks to extract features from W

257 ature descriptors for the porosity) to train convolutional neural networks to predict gas separation

258 ere we use a scalable tactile glove and deep convolutional neural networks to show that sensors unifo

259 the application of machine learning, namely, convolutional neural networks to solve problems in the i

260 ep learning platform, consisting of multiple convolutional neural networks, to classify pathologic im

261 ep learning-specifically, the application of convolutional neural networks-to radiologic imaging that

262 The resulting ROIs were processed using a convolutional neural network trained on an independent c

263 also outperforms a deep neural network and a convolutional neural network trained on identical data.

264 Here, we show that a convolutional neural network trained using a generative

265 calcification (TAC) were quantified using a convolutional neural network trained with (a) 1181 low-d

266 se PLA General Hospital, China, using a deep convolutional neural network trained with 2,123 pixel-le

267 Convolutional neural networks trained on genome-wide CpG

268 ence alignments (MSAs) through deep residual convolutional neural network training.

269 In this retrospective study, a convolutional neural network (trauma hand radiograph-tra

270 rediction models, the Lung Cancer Prediction Convolutional Neural Network was associated with improve

271 if they were 4 channels of a single image.A convolutional neural network was built and trained on th

272 A multilabel convolutional neural network was designed to accurately

273 In this work, a fully convolutional neural network was implemented as a fast a

274 Conclusion A convolutional neural network was superior to radiomic an

275 Methods In this retrospective study, a deep convolutional neural network was trained to assess Breas

276 A deep learning convolutional neural network was trained to assess fundu

277 A DL convolutional neural network was trained to assess optic

278 A convolutional neural network was trained to automaticall

279 A DL convolutional neural network was trained to predict the

280 A convolutional neural network was trained to predict tumo

281 A multitask deep convolutional neural network was trained to provide biop

282 For training data (n = 109), a U-Net convolutional neural network was trained to segment the

283 A convolutional neural network was used for classification

284 h probability maps generated through a fully convolutional neural network was used to segment drusen

285 Methods SmartRhythm 2.0, a convolutional neural network, was trained on anonymized

286 Using a sparse Convolutional Neural Network we identified 132 million i

287 Support vector machines and convolutional neural networks were trained to 2 end poin

288 Three-dimensional convolutional neural networks were trained to estimate g

289 ge-based approach that uses state-of-the-art convolutional neural networks, where the algorithm is le

290 of positive selection, ImaGene implements a convolutional neural network which is trained using simu

291 SK outperforms character-level recurrent and convolutional neural networks while achieving low varian

292 We trained a fully convolutional neural network with 4,396 head CT scans pe

293 oped a computer-aided approach which combing convolutional neural network with machine classifier to

294 ametric MRI data as a sequence input for the convolutional neural network with the recurrent neural n

295 ep takes raw sequence data as input and uses convolutional neural networks with a novel two-dimension

296 the correlation between different layers of convolutional neural networks with EEG and Face images a

297 substrate sequence data as input and employs convolutional neural networks with transfer learning to

298 We trained a convolutional neural network, with a VGG architecture (i

299 or a combined word- and sentence-level input convolutional neural network (ws-CNN).

300 ning CHA(2)DS(2)-VASc with random forest and convolutional neural network yielded a validation AUC of