ライフサイエンスコーパス: convolutional

1 rning approach, which combined deep-learning convolutional and hand-crafted, lumen morphological feat

2 the difference between the models learned by convolutional and recurrent networks.

3 An additional advantage of our fully convolutional architecture is that it allows for trainin

4 The convolutional architecture of the CNN captures contextua

5 nce data was found to outperform the popular convolutional CNN with a single MRI sequence.

6 Here, we present a graph convolutional deep neural network (DNN) model, trained o

7 chanism detection through the use of a fully convolutional deep neural network architecture (F-CNN).

8 We developed a convolutional deep neural network-based approach named D

9 ng the innovative deep learning approach and convolutional deep neural networks (cDNNs), ResNet50 or

10 Using convolutional deep neural networks, the supervised machi

11 < 0.05), as compared to classification with convolutional features alone.

12 network was based on DenseNet-161, a shared convolutional features extractor trained with multitask

13 to reveal how CNN architecture, specifically convolutional filter size and max-pooling, influences th

14 ting k-mer based methods by jointly learning convolutional filters and k-mer embeddings to represent

15 t the deep learning models by converting the convolutional filters into sequence logos and quantitati

16 age processing, we 'un-box' our models using convolutional filters, attention maps, and in silico mut

17 This manuscript proposes a novel convolutional framework based on MobileNetV2 and connect

18 synaptic functions to facilitate concurrent convolutional inference and correlative learning efficie

19 Besides, we visualize the convolutional kernels and successfully identify the key

20 (3D) and the other with two-dimensional (2D) convolutional kernels.

21 By visualizing the filters in convolutional layers and saliency maps, we find that the

22 ect on model performance even when there are convolutional layers implied.

23 ilarity analysis, we compared activations of convolutional layers of a DCNN trained for object and sc

24 nvolutional network, which contains multiple convolutional layers of varying filter lengths, could ut

25 use a graph autoencoder consisting of graph convolutional layers to predict relationships between si

26 all latent features captured by the multiple convolutional layers.

27 We introduce an approach based on deep convolutional long short term memory (ConvLSTM) to predi

28 clinical data to assess the performance of a convolutional long short-term memory (LSTM) neural netwo

29 A convolutional LSTM architecture can capture local and gl

30 The convolutional LSTM network demonstrated 91% to 93% accur

31 neural network models outperform feedforward convolutional models matched in their number of paramete

32 semble empirical mode decomposition-temporal convolutional network (EEMD-TCN) hybrid approach, which

33 ate-of-the-art DL models including the fully convolutional network (FCN), SegNet, Dilated-Net, origin

34 tional network consisting of a uniform graph convolutional network and multiple subnetworks.

35 cision and robustness compared with existing convolutional network approaches to behavioral tracking.

36 The authors improved the Btrfly Net, a fully convolutional network architecture described by Sekuboyi

37 Specifically, DeepCDR is a hybrid graph convolutional network consisting of a uniform graph conv

38 n this work, we introduce ChromeGCN, a graph convolutional network for chromatin profile prediction b

39 sent here TrailMap, a three-dimensional (3D) convolutional network for extracting axonal projections

40 oyed the embedding layer and the multi-scale convolutional network in our model.

41 Multi-scale convolutional network is a novel addition to existing de

42 We train a deep convolutional network using a dataset of 4,339,879 annot

43 and advanced graph-based models (e.g., graph convolutional network).

44 Our framework linked a fully convolutional network, which constructs high resolution

45 The multi-scale convolutional network, which contains multiple convoluti

46 More specifically, we present a fully convolutional network-based regression model and extensi

47 n the deep learning Fast learning with Graph Convolutional Networks (FastGCN) algorithm to predict th

48 Graph convolutional networks (GCN) can capture such neighborho

49 uce the theoretical foundations behind graph convolutional networks and illustrate various architectu

50 ematic review on the emerging field of graph convolutional networks and their applications in drug di

51 ation deep learning algorithms such as fully convolutional networks stand out for their accuracy, com

52 s and future possibilities of applying graph convolutional networks to drug discovery.

53 ge with components that use pre-trained deep convolutional networks to profile images with vectors of

54 Fully convolutional networks trained using these annotations w

55 achine learning approach consisting of graph convolutional networks used to extract molecular shape f

56 We show that DoGNet outperforms convolutional networks with a low-to-moderate number of

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

106 The convolutional neural network allowed high-throughput and

107 The convolutional neural network also showed high performanc

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

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

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

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

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

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

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

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

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

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

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

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

120 The proposed convolutional neural network enables accurate artery and

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

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

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

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

125 A deep convolutional neural network for image quality assessmen

126 We trained a convolutional neural network for multiclass segmentation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

151 Convolutional neural network of InceptionV3 architecture

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

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

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

155 The convolutional neural network that was previously derived

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

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

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

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

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

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

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

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

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

165 PlantSeg employs a convolutional neural network to predict cell boundaries

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

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

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

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

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

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

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

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

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

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

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

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

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

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

180 A multilabel convolutional neural network was designed to accurately

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

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

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

184 A DL convolutional neural network was trained to assess optic

185 A convolutional neural network was trained to automaticall

186 A convolutional neural network was trained to predict tumo

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

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

189 A convolutional neural network was used for classification

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

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

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

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

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

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

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

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

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

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

200 The convolutional neural network, DeepMedic, was trained on

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

228 Convolutional neural networks (CNNs) have achieved human

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

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

231 Convolutional Neural Networks (CNNs) have been successfu

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

251 Deep convolutional neural networks (DCNNs) are frequently des

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

253 Deep convolutional neural networks (DCNNs) were trained indep

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

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

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

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

258 Convolutional neural networks can solve this generalized

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

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

261 Deep convolutional neural networks for images and long short-

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

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

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

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

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

267 Two different convolutional neural networks models (MobileNet and Ince

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

269 Analysis powered by trained convolutional neural networks precisely identified featu

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

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

272 We used fully convolutional neural networks to automatically detect se

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

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

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

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

277 Convolutional neural networks trained on genome-wide CpG

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

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

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

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

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

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

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

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

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

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

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

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

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

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

292 ned and abnormality features extracted using convolutional neural networks.

293 Both algorithms used one-dimensional convolutional neural networks.

294 Computational methods, including convolutional neuronal networks, and other machine learn

295 neural network composed of both 1 D and 2 D convolutional residual neural networks (ResNet).

296 A convolutional Siamese neural network-based algorithm was

297 resistors are reported to emulate the analog convolutional signal processing, correlative learning, a

298 d whether an artificial neural network (with convolutional structure) trained for visual categorizati

299 ompositional functions, deep networks of the convolutional type (even without weight sharing) can avo

300 Methods: We constructed a deep convolutional U-net-shaped neural network for generation