Filters








899 Hits in 7.4 sec

A Tensor Space Model-Based Deep Neural Network for Text Classification

Han-joon Kim, Pureum Lim
2021 Applied Sciences  
Recently, given developments in deep learning technology, several scholars have used deep neural networks (recurrent and convolutional neural networks) to improve text classification.  ...  The semantic tensor used for text representation features a dependency between the term and the concept; we use this to develop three deep neural networks for text classification.  ...  Recently, recurrent neural networks (RNNs) or convolutional neural networks (CNNs) have been used to improve text classification systems.  ... 
doi:10.3390/app11209703 fatcat:s5bp54tdu5djtmuzreehowk5om

Front Matter: Volume 12032

Ivana Išgum, Olivier Colliot
2022 Medical Imaging 2022: Image Processing  
.  The last two digits indicate publication order within the volume using a Base 36 numbering system employing both numerals and letters. These two-number sets start with 00,  ...  Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication.  ...  transformer-based semantic segmentation networks for pathological image segmentation [12032-128] 3O Automatic lung segmentation in dynamic thoracic MRI using two-stage deep convolutional neural networks  ... 
doi:10.1117/12.2638192 fatcat:ikfgnjefaba2tpiamxoftyi6sa

A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
for Histopathology 351 Combining Convolutional and Recurrent Neural Networks for Classification of Focal Liver Lesions in Multi-Phase CT Images 352 Domain and Geometry Agnostic CNNs for Left Atrium Segmentation  ...  of Functional Networks and Structural Connectivity 560 Ultra-fast T2-weighted MR Reconstruction using Complementary T1-weighted Information 562 Deep Convolutional Gaussian Mixture Model for Stain-Color  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

A Survey on Deep Learning Architectures and Frameworks for Cancer Detection in Medical Images Analysis

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
The study includes some dominant deep learning algorithms such as convolution neural network, fully convolutional network, autoencoder, and deep belief network to analyze the medical image and to detect  ...  Deep learning contributes to enhanced performance and better prediction in detection of cancer with medical images.  ...  Enhanced image detection and classification using convolutional neural network (CNN) models applied on 3D lung CT scan images with few phases on the proposed work [19] .  ... 
doi:10.35940/ijitee.k7654.0991120 fatcat:4qkd3kdqvjgu7n2g7wnmnyiici

Unifying Neural Learning and Symbolic Reasoning for Spinal Medical Report Generation [article]

Zhongyi Han, Benzheng Wei, Yilong Yin, Shuo Li
2020 arXiv   pre-print
In this paper, we propose the neural-symbolic learning (NSL) framework that performs human-like learning by unifying deep neural learning and symbolic logical reasoning for the spinal medical report generation  ...  segmentation of spinal structures with high complexity and variability.  ...  We compare the semantic segmentation ability of our neural symbolic learning framework (NSL) with several state-of-the-art semantic segmentation networks as follows. • Fully Convolutional Network (FCN)  ... 
arXiv:2004.13577v1 fatcat:oh5aka5zr5be3ipd7qqnyhikzy

Deep CNN and Deep GAN in Computational Visual Perception-Driven Image Analysis

R. Nandhini Abirami, P. M. Durai Raj Vincent, Kathiravan Srinivasan, Usman Tariq, Chuan-Yu Chang, Dr Shahzad Sarfraz
2021 Complexity  
Deep convolutional neural networks' applications, namely, image classification, localization and detection, document analysis, and speech recognition, are discussed in detail.  ...  The survey explores various deep learning techniques adapted to solve computer vision problems using deep convolutional neural networks and deep generative adversarial networks.  ...  Deep Convolutional Neural Network. e deep convolutional neural networks, popularly known as D-CNN or D-ConvNets, are a robust ANN class and are the most established deep learning algorithm that have become  ... 
doi:10.1155/2021/5541134 fatcat:xluxbl7kojbvxpjq5u726d3djm

Generation and Simulation of Yeast Microscopy Imagery with Deep Learning [article]

Christoph Reich
2021 arXiv   pre-print
To approach this problem, a novel generative adversarial network, for conditionalized and unconditionalized image generation, is proposed.  ...  This thesis is a study towards deep learning-based modeling of TLFM experiments on the image level.  ...  Ronneberger et al. published a paper of a convolutional neural network specially developed for biological and medical imaging.  ... 
arXiv:2103.11834v4 fatcat:x6jljdhd4vcx7a3wjxj6igs57q

A Survey on Content Based Image Retrieval Using Convolutional Neural Networks

2020 International Journal of Advanced Trends in Computer Science and Engineering  
It also focuses on content based image retrieval technique (CBIR), with an unsupervised learning method using convolutional Neural Networks (CNN).  ...  A smart image retrieval technique has been an increasing demand by the advancements in the field of computer networks and mobile computing.  ...  With the vast development, unsolved problem of semantic relevance can be a solution with the help of deep convolutional neural networks and deep hashing techniques.  ... 
doi:10.30534/ijatcse/2020/70952020 fatcat:vjpq2j2pdza5di426baglhavai

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 265-276 Multi-View Image Classification With Visual, Semantic and View Consistency.  ...  Ge, S., Ensemble of Deep Convolutional Neural Networks With Gabor Face Representations for Face Recognition.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

A Graphical Approach For Brain Haemorrhage Segmentation [article]

Dr. Ninad Mehendale, Pragya Gupta, Nishant Rajadhyaksha, Ansh Dagha, Mihir Hundiwala, Aditi Paretkar, Sakshi Chavan, Tanmay Mishra
2022 arXiv   pre-print
In this paper, we propose a novel implementation of an architecture consisting of traditional Convolutional Neural Networks(CNN) along with Graph Neural Networks(GNN) to produce a holistic model for the  ...  Recent advances in Deep Learning and Image Processing have utilised different modalities like CT scans to help automate the detection and segmentation of brain haemorrhage occurrences.  ...  Graph Neural Networks have recently been used in medical imaging cases.  ... 
arXiv:2202.06876v1 fatcat:ydqrth4yqbexpmmxgh3rss44rm

Pattern Classification Approaches for Breast Cancer Identification via MRI: State-Of-The-Art and Vision for the Future

Xiao-Xia Yin, Lihua Yin, Sillas Hadjiloucas
2020 Applied Sciences  
well as Clifford algebra based classification approaches and convolutional neural network deep learning methodologies.  ...  The proposed framework can potentially reduce the costs associated with the interpretation of medical images by providing automated, faster and more consistent diagnosis.  ...  In the field of medical image analysis and classification, GoogLeNet [70] , a multi-scale residual network (MSRN) [71] , U-Net neural network model [64] , class structure-based deep convolutional neural  ... 
doi:10.3390/app10207201 fatcat:tofpvyllzbautos4my26xajqfe

MAMA Net: Multi-scale Attention Memory Autoencoder Network for Anomaly Detection

Yurong Chen, Hui Zhang, Yaonan Wang, Yimin Yang, Xianen Zhou, Q.M. Jonathan Wu
2020 IEEE Transactions on Medical Imaging  
plugged into any networks as sampling, upsampling and downsampling function.  ...  paper, we propose a Multi-scale Attention Memory with hash addressing Autoencoder network (MAMA Net) for anomaly detection.  ...  algorithm [7] , [8] , such as support vector machines (SVMs) [7] , and neural networks one-class classification methods: deep one-class (DOC) [8] and so on.  ... 
doi:10.1109/tmi.2020.3045295 fatcat:elicefjf45hunafl7oamhvx2na

Locality Guided Neural Networks for Explainable Artificial Intelligence [article]

Randy Tan, Naimul Khan, Ling Guan
2020 arXiv   pre-print
In our experiments, we train various VGG and Wide ResNet (WRN) networks for image classification on CIFAR100.  ...  This paper focuses on Convolutional Neural Networks (CNNs), but can theoretically be applied to any type of deep learning architecture.  ...  While in this work we focus on convolutional neural networks with images, this method is theoretically applicable to other networks types.  ... 
arXiv:2007.06131v1 fatcat:6nauo3hblbhb5irusdi3dhc3dq

Adaptation and contextualization of deep neural network models

Dimitrios Kollias, Miao Yu, Athanasios Tagaris, Georgios Leontidis, Andreas Stafylopatis, Stefanos Kollias
2017 2017 IEEE Symposium Series on Computational Intelligence (SSCI)  
The ability of Deep Neural Networks (DNNs) to provide very high accuracy in classification and recognition problems makes them the major tool for developments in such problems.  ...  The main memory is composed of the trained DNN architecture for classification/prediction, i.e., its structure and weights, as well as of an extracted -equivalent -Clustered Representation Set (CRS) generated  ...  We perform transfer learning of the weights of the convolutional and pooling part of VGG or ResNet network to our neural architecture.  ... 
doi:10.1109/ssci.2017.8280975 dblp:conf/ssci/KolliasYTLSK17 fatcat:ckdelnwsm5cgxh5u652nkzyvxa

A Comprehensive Review on Deep Supervision: Theories and Applications [article]

Renjie Li, Xinyi Wang, Guan Huang, Wenli Yang, Kaining Zhang, Xiaotong Gu, Son N. Tran, Saurabh Garg, Jane Alty, Quan Bai
2022 arXiv   pre-print
We propose a new classification of different deep supervision networks, and discuss advantages and limitations of current deep supervision networks in computer vision applications.  ...  This technique has been increasingly applied in deep neural network learning systems for various computer vision applications recently.  ...  Fully Convolutional Network (FCN) [5] achieves success in semantic segmentation, while U-Net [6] lays the foundation for medical image segmentation.  ... 
arXiv:2207.02376v1 fatcat:wfi2ribjrrbcdglsbmg5t7jp74
« Previous Showing results 1 — 15 out of 899 results