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69 results on '"Yang, Xiaokang"'

1. A deep learning system for predicting time to progression of diabetic retinopathy.

Author

Dai L, Sheng B, Chen T, Wu Q, Liu R, Cai C, Wu L, Yang D, Hamzah H, Liu Y, Wang X, Guan Z, Yu S, Li T, Tang Z, Ran A, Che H, Chen H, Zheng Y, Shu J, Huang S, Wu C, Lin S, Liu D, Li J, Wang Z, Meng Z, Shen J, Hou X, Deng C, Ruan L, Lu F, Chee M, Quek TC, Srinivasan R, Raman R, Sun X, Wang YX, Wu J, Jin H, Dai R, Shen D, Yang X, Guo M, Zhang C, Cheung CY, Tan GSW, Tham YC, Cheng CY, Li H, Wong TY, and Jia W

Subjects
Humans, Blindness, Diabetic Retinopathy diagnosis, Deep Learning, Diabetes Mellitus
Abstract

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals., (© 2024. The Author(s).)

Published
2024
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2. A deep learning system for detecting diabetic retinopathy across the disease spectrum.

Author

Dai L, Wu L, Li H, Cai C, Wu Q, Kong H, Liu R, Wang X, Hou X, Liu Y, Long X, Wen Y, Lu L, Shen Y, Chen Y, Shen D, Yang X, Zou H, Sheng B, and Jia W

Subjects
Aged, Datasets as Topic, Diabetes Mellitus, Type 2 complications, Diabetic Retinopathy etiology, Diabetic Retinopathy pathology, Humans, ROC Curve, Deep Learning, Diabetic Retinopathy diagnosis, Fundus Oculi, Image Interpretation, Computer-Assisted methods, Severity of Illness Index
Abstract

Retinal screening contributes to early detection of diabetic retinopathy and timely treatment. To facilitate the screening process, we develop a deep learning system, named DeepDR, that can detect early-to-late stages of diabetic retinopathy. DeepDR is trained for real-time image quality assessment, lesion detection and grading using 466,247 fundus images from 121,342 patients with diabetes. Evaluation is performed on a local dataset with 200,136 fundus images from 52,004 patients and three external datasets with a total of 209,322 images. The area under the receiver operating characteristic curves for detecting microaneurysms, cotton-wool spots, hard exudates and hemorrhages are 0.901, 0.941, 0.954 and 0.967, respectively. The grading of diabetic retinopathy as mild, moderate, severe and proliferative achieves area under the curves of 0.943, 0.955, 0.960 and 0.972, respectively. In external validations, the area under the curves for grading range from 0.916 to 0.970, which further supports the system is efficient for diabetic retinopathy grading.

Published
2021
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6. A Hierarchical Clustering Obstacle Detection Method Applied to RGB-D Cameras.

Author

Liu, Chunyang, Xie, Saibao, Ma, Xiqiang, Huang, Yan, Sui, Xin, Guo, Nan, Yang, Fang, and Yang, Xiaokang

Subjects
HIERARCHICAL clustering (Cluster analysis), CAMERAS, FEATURE extraction, DEEP learning, ROBOT motion, GABOR filters
Abstract

Environment perception is a key part of robot self-controlled motion. When using vision to accomplish obstacle detection tasks, it is difficult for deep learning methods to detect all obstacles due to complex environment and vision limitations, and it is difficult for traditional methods to meet real-time requirements when applied to embedded platforms. In this paper, a fast obstacle-detection process applied to RGB-D cameras is proposed. The process has three main steps, feature point extraction, noise removal, and obstacle clustering. Using Canny and Shi–Tomasi algorithms to complete the pre-processing and feature point extraction, filtering noise based on geometry, grouping obstacles with different depths based on the basic principle that the feature points on the same object contour must be continuous or within the same depth in the view of RGB-D camera, and then doing further segmentation from the horizontal direction to complete the obstacle clustering work. The method omits the iterative computation process required by traditional methods and greatly reduces the memory and time overhead. After experimental verification, the proposed method has a comprehensive recognition accuracy of 82.41%, which is 4.13% and 19.34% higher than that of RSC and traditional methods, respectively, and recognition accuracy of 91.72% under normal illumination, with a recognition speed of more than 20 FPS on the embedded platform; at the same time, all detections can be achieved within 1 m under normal illumination, and the detection error is no more than 2 cm within 3 m. [ABSTRACT FROM AUTHOR]

Published
2023
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7. Residual Quantization for Low Bit-Width Neural Networks.

Author

Li, Zefan, Ni, Bingbing, Yang, Xiaokang, Zhang, Wenjun, and Gao, Wen

Abstract

Neural network quantization has shown to be an effective way for network compression and acceleration. However, existing binary or ternary quantization methods suffer from two major issues. First, low bit-width input/activation quantization easily results in severe prediction accuracy degradation. Second, network training and quantization are always treated as two non-related tasks, leading to accumulated parameter training error and quantization error. In this work, we introduce a novel scheme, named Residual Quantization, to train a neural network with both weights and inputs constrained to low bit-width, e.g., binary or ternary values. On one hand, by recursively performing residual quantization, the resulting binary/ternary network is guaranteed to approximate the full-precision network with much smaller errors. On the other hand, we mathematically re-formulate the network training scheme in an EM-like manner, which iteratively performs network quantization and parameter optimization. During expectation, the low bit-width network is encouraged to approximate the full-precision network. During maximization, the low bit-width network is further tuned to gain better representation capability. Extensive experiments well demonstrate that the proposed quantization scheme outperforms previous low bit-width methods and achieves much closer performance to the full-precision counterpart. [ABSTRACT FROM AUTHOR]

Published
2023
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8. Neural Graph Matching Network: Learning Lawler’s Quadratic Assignment Problem With Extension to Hypergraph and Multiple-Graph Matching.

Author

Wang, Runzhong, Yan, Junchi, and Yang, Xiaokang

Subjects
QUADRATIC assignment problem, COMBINATORIAL optimization, CHARTS, diagrams, etc., PEER-to-peer architecture (Computer networks)
Abstract

Graph matching involves combinatorial optimization based on edge-to-edge affinity matrix, which can be generally formulated as Lawler's quadratic assignment problem (QAP). This paper presents a QAP network directly learning with the affinity matrix (equivalently the association graph) whereby the matching problem is translated into a constrained vertex classification task. The association graph is learned by an embedding network for vertex classification, followed by Sinkhorn normalization and a cross-entropy loss for end-to-end learning. We further improve the embedding model on association graph by introducing Sinkhorn based matching-aware constraint, as well as dummy nodes to deal with unequal sizes of graphs. To our best knowledge, this is one of the first network to directly learn with the general Lawler's QAP. In contrast, recent deep matching methods focus on the learning of node/edge features in two graphs respectively. We also show how to extend our network to hypergraph matching, and matching of multiple graphs. Experimental results on both synthetic graphs and real-world images show its effectiveness. For pure QAP tasks on synthetic data and QAPLIB benchmark, our method can perform competitively and even surpass state-of-the-art graph matching and QAP solvers with notable less time cost. We provide a project homepage at http://thinklab.sjtu.edu.cn/project/NGM/index.html. [ABSTRACT FROM AUTHOR]

Published
2022
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9. Deep Object Tracking With Shrinkage Loss.

Author

Lu, Xiankai, Ma, Chao, Shen, Jianbing, Yang, Xiaokang, Reid, Ian, and Yang, Ming-Hsuan

Subjects
OBJECT tracking (Computer vision), DEEP learning, DATA distribution, TRACKING algorithms
Abstract

In this paper, we address the issue of data imbalance in learning deep models for visual object tracking. Although it is well known that data distribution plays a crucial role in learning and inference models, considerably less attention has been paid to data imbalance in visual tracking. For the deep regression trackers that directly learn a dense mapping from input images of target objects to soft response maps, we identify their performance is limited by the extremely imbalanced pixel-to-pixel differences when computing regression loss. This prevents existing end-to-end learnable deep regression trackers from performing as well as discriminative correlation filters (DCFs) trackers. For the deep classification trackers that draw positive and negative samples to learn discriminative classifiers, there exists heavy class imbalance due to a limited number of positive samples when compared to the number of negative samples. To balance training data, we propose a novel shrinkage loss to penalize the importance of easy training data mostly coming from the background, which facilitates both deep regression and classification trackers to better distinguish target objects from the background. We extensively validate the proposed shrinkage loss function on six benchmark datasets, including the OTB-2013, OTB-2015, UAV-123, VOT-2016, VOT-2018 and LaSOT. Equipped with our shrinkage loss, the proposed one-stage deep regression tracker achieves favorable results against state-of-the-art methods, especially in comparison with DCFs trackers. Meanwhile, our shrinkage loss generalizes well to deep classification trackers. When replacing the original binary cross entropy loss with our shrinkage loss, three representative baseline trackers achieve large performance gains, even setting new state-of-the-art results. [ABSTRACT FROM AUTHOR]

Published
2022
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10. Saliency prediction on omnidirectional images with attention-aware feature fusion network.

Author

Zhu, Dandan, Chen, Yongqing, Zhao, Defang, Zhou, Qiangqiang, and Yang, Xiaokang

Subjects
DEEP learning, PROBLEM solving, FORECASTING
Abstract

Recent years have witnessed rapid development of deep learning technology and its successful application in the saliency prediction of traditional 2D images. However, when using deep neural network (DNN) models to perform saliency prediction on omnidirectional images (ODIs), there are two critical issues: (1) The datasets for ODIs are small-scale that cannot support the training DNN-based models. (2) It is challenging to perform saliency prediction in that some ODIs contain complex background clutters. In order to solve these two problems, we propose a novel Attention-Aware Features Fusion Network (AAFFN) model which is first trained with traditional 2D images and then transferred to the ODIs for saliency prediction. Specifically, our proposed AAFFN model consists of three modules: a Part-guided Attention (PA) module, a Visibility Score (VS) module, and a Attention-Aware Features Fusion (AAFF) module. The PA module is used to extract precise features to estimate attention of the finer part on ODIs, and eliminate the influence of cluttered background. Meanwhile, the VS module is introduced to measure the proportion of the foreground and background parts and generate visibility scores in the feature learning process. Finally, in the AAFF module, we utilize the weighted fusion of attention maps and visibility scores to generate the final saliency map. Extensive experiments and ablation analysis demonstrate that the proposed model achieves superior performance and outperforms other state-of-the-art methods on public benchmark datasets. [ABSTRACT FROM AUTHOR]

Published
2021
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11. STFlow: Self-Taught Optical Flow Estimation Using Pseudo Labels.

Author

Ren, Zhe, Luo, Wenhan, Yan, Junchi, Liao, Wenlong, Yang, Xiaokang, Yuille, Alan, and Zha, Hongyuan

Subjects
OPTICAL flow, ARTIFICIAL neural networks, DEEP learning, OPTICAL fiber networks, SATISFACTION, ADAPTIVE optics
Abstract

The Deep learning of optical flow has been an active area for its empirical success. For the difficulty of obtaining accurate dense correspondence labels, unsupervised learning of optical flow has drawn more and more attention, while the accuracy is still far from satisfaction. By holding the philosophy that better estimation models can be trained with better-approximated labels, which in turn can be obtained from better estimation models, we propose a self-taught learning framework to continually improve the accuracy using self-generated pseudo labels. The estimated optical flow is first filtered by bidirectional flow consistency validation and occlusion-aware dense labels are then generated by edge-aware interpolation from selected sparse matches. Moreover, by combining reconstruction loss with regression loss on the generated pseudo labels, the performance is further improved. The experimental results demonstrate that our models achieve state-of-the-art results among unsupervised methods on the public KITTI, MPI-Sintel and Flying Chairs datasets. [ABSTRACT FROM AUTHOR]

Published
2020
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12. A Multimodal Saliency Model for Videos With High Audio-Visual Correspondence.

Author

Min, Xiongkuo, Zhai, Guangtao, Zhou, Jiantao, Zhang, Xiao-Ping, Yang, Xiaokang, and Guan, Xinping

Subjects
MULTIMODAL user interfaces, AUDIOVISUAL materials, DEEP learning, EYE tracking, VIDEOS, STATISTICAL correlation
Abstract

Audio information has been bypassed by most of current visual attention prediction studies. However, sound could have influence on visual attention and such influence has been widely investigated and proofed by many psychological studies. In this paper, we propose a novel multi-modal saliency (MMS) model for videos containing scenes with high audio-visual correspondence. In such scenes, humans tend to be attracted by the sound sources and it is also possible to localize the sound sources via cross-modal analysis. Specifically, we first detect the spatial and temporal saliency maps from the visual modality by using a novel free energy principle. Then we propose to detect the audio saliency map from both audio and visual modalities by localizing the moving-sounding objects using cross-modal kernel canonical correlation analysis, which is first of its kind in the literature. Finally we propose a new two-stage adaptive audiovisual saliency fusion method to integrate the spatial, temporal and audio saliency maps to our audio-visual saliency map. The proposed MMS model has captured the influence of audio, which is not considered in the latest deep learning based saliency models. To take advantages of both deep saliency modeling and audio-visual saliency modeling, we propose to combine deep saliency models and the MMS model via a later fusion, and we find that an average of 5% performance gain is obtained. Experimental results on audio-visual attention databases show that the introduced models incorporating audio cues have significant superiority over state-of-the-art image and video saliency models which utilize a single visual modality. [ABSTRACT FROM AUTHOR]

Published
2020
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13. BE-CALF: Bit-Depth Enhancement by Concatenating All Level Features of DNN.

Author

Liu, Jing, Sun, Wanning, Su, Yuting, Jing, Peiguang, and Yang, Xiaokang

Subjects
DEEP learning, HIGH dynamic range imaging
Abstract

There is a growing demand for monitors to provide high-quality visualization with more bits representing each rendered pixel. However, since most existing images and videos are of low bit-depth (LBD), transforming LBD images to visually pleasant high bit-depth (HBD) versions is of significant value. Most existing bit-depth enhancement methods generate unsatisfactory HBD images with annoying false contour artifacts or blurry details, and some algorithms are also time-consuming. To overcome these drawbacks, we propose a bit-depth enhancement framework via concatenating all level features of deep neural networks (DNNs). A novel deep learning network is proposed based on the deep convolutional variational auto-encoders (VAEs), and skip connections that concatenate every two layers are applied to pass low-level and high-level features to consequent layers, easing the gradient vanishing problem. Meanwhile, the proposed network is optimized to generate the residual between original images and its quantized ones, which performs better than recovering HBD images directly. The experimental results show that the proposed algorithm can eliminate false contour artifacts of the recovered HBD images with low time consumption, and can achieve dramatic restoration performance gains compared with state-of-the-art methods both subjectively and objectively. [ABSTRACT FROM AUTHOR]

Published
2019
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14. Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking.

Author

Yu, Jun, Yang, Xiaokang, Gao, Fei, and Tao, Dacheng

Abstract

How do we retrieve images accurately? Also, how do we rank a group of images precisely and efficiently for specific queries? These problems are critical for researchers and engineers to generate a novel image searching engine. First, it is important to obtain an appropriate description that effectively represent the images. In this paper, multimodal features are considered for describing images. The images unique properties are reflected by visual features, which are correlated to each other. However, semantic gaps always exist between images visual features and semantics. Therefore, we utilize click feature to reduce the semantic gap. The second key issue is learning an appropriate distance metric to combine these multimodal features. This paper develops a novel deep multimodal distance metric learning (Deep-MDML) method. A structured ranking model is adopted to utilize both visual and click features in distance metric learning (DML). Specifically, images and their related ranking results are first collected to form the training set. Multimodal features, including click and visual features, are collected with these images. Next, a group of autoencoders is applied to obtain initially a distance metric in different visual spaces, and an MDML method is used to assign optimal weights for different modalities. Next, we conduct alternating optimization to train the ranking model, which is used for the ranking of new queries with click features. Compared with existing image ranking methods, the proposed method adopts a new ranking model to use multimodal features, including click features and visual features in DML. We operated experiments to analyze the proposed Deep-MDML in two benchmark data sets, and the results validate the effects of the method. [ABSTRACT FROM PUBLISHER]

Published
2017
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15. Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy.

Author

Liu, Pingping, Yang, Xiaokang, Jin, Baixin, and Zhou, Qiuzhan

Subjects
*CONVOLUTIONAL neural networks, *DEEP learning, *ARTIFICIAL neural networks, *ENTROPY, *DIABETIC retinopathy, *IMAGE processing
Abstract

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network's ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency. [ABSTRACT FROM AUTHOR]

Published
2021
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16. Standardising Wound Image Acquisition Through Edge AI

Author

Vasconcelos, Maria João M., Sampaio, Ana Filipa, Cardoso, Nuno, Liberal, Marcos, Alves, Paulo, Marques, Raquel, Salgado, Pedro, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Chen, Phoebe, Editorial Board Member, Cuzzocrea, Alfredo, Editorial Board Member, Du, Xiaoyong, Editorial Board Member, Kara, Orhun, Editorial Board Member, Liu, Ting, Editorial Board Member, Sivalingam, Krishna M., Editorial Board Member, Slezak, Dominik, Editorial Board Member, Washio, Takashi, Editorial Board Member, Yang, Xiaokang, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Ziefle, Martina, editor, Lozano, María Dolores, editor, and Mulvenna, Maurice, editor

Published
2024
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17. vEEGNet: Learning Latent Representations to Reconstruct EEG Raw Data via Variational Autoencoders

Author

Zancanaro, Alberto, Cisotto, Giulia, Zoppis, Italo, Manzoni, Sara Lucia, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Chen, Phoebe, Editorial Board Member, Cuzzocrea, Alfredo, Editorial Board Member, Du, Xiaoyong, Editorial Board Member, Kara, Orhun, Editorial Board Member, Liu, Ting, Editorial Board Member, Sivalingam, Krishna M., Editorial Board Member, Slezak, Dominik, Editorial Board Member, Washio, Takashi, Editorial Board Member, Yang, Xiaokang, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Ziefle, Martina, editor, Lozano, María Dolores, editor, and Mulvenna, Maurice, editor

Published
2024
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19. Multi-view Object Recognition Based on Deep Models

Author

Li, Xin, Ai, Shaojie, Liu, Jing, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhai, Guangtao, editor, Zhou, Jun, editor, Ye, Long, editor, Yang, Hua, editor, An, Ping, editor, and Yang, Xiaokang, editor

Published
2024
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22. A Novel Neural Network-Based Recommender System for Drug Recommendation

Author

Al Mubasher, Hadi, Doughan, Ziad, Sliman, Layth, Haidar, Ali, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Chen, Phoebe, Editorial Board Member, Cuzzocrea, Alfredo, Editorial Board Member, Du, Xiaoyong, Editorial Board Member, Kara, Orhun, Editorial Board Member, Liu, Ting, Editorial Board Member, Sivalingam, Krishna M., Editorial Board Member, Slezak, Dominik, Editorial Board Member, Washio, Takashi, Editorial Board Member, Yang, Xiaokang, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Iliadis, Lazaros, editor, Maglogiannis, Ilias, editor, Alonso, Serafin, editor, Jayne, Chrisina, editor, and Pimenidis, Elias, editor

Published
2023
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23. Residual Error Learning for Electricity Demand Forecasting

Author

Andronikos, Achilleas, Tzelepi, Maria, Tefas, Anastasios, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Chen, Phoebe, Editorial Board Member, Cuzzocrea, Alfredo, Editorial Board Member, Du, Xiaoyong, Editorial Board Member, Kara, Orhun, Editorial Board Member, Liu, Ting, Editorial Board Member, Sivalingam, Krishna M., Editorial Board Member, Slezak, Dominik, Editorial Board Member, Washio, Takashi, Editorial Board Member, Yang, Xiaokang, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Iliadis, Lazaros, editor, Maglogiannis, Ilias, editor, Alonso, Serafin, editor, Jayne, Chrisina, editor, and Pimenidis, Elias, editor

Published
2023
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24. Virtual Flow Meter for an Industrial Process

Author

González-Herbón, Raúl, González-Mateos, Guzmán, Alonso, Serafín, Prada, Miguel A., Fuertes, Juan J., Morán, Antonio, Domínguez, Manuel, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Chen, Phoebe, Editorial Board Member, Cuzzocrea, Alfredo, Editorial Board Member, Du, Xiaoyong, Editorial Board Member, Kara, Orhun, Editorial Board Member, Liu, Ting, Editorial Board Member, Sivalingam, Krishna M., Editorial Board Member, Slezak, Dominik, Editorial Board Member, Washio, Takashi, Editorial Board Member, Yang, Xiaokang, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Iliadis, Lazaros, editor, Maglogiannis, Ilias, editor, Alonso, Serafin, editor, Jayne, Chrisina, editor, and Pimenidis, Elias, editor

Published
2023
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25. Towards Automatic Assessment of Quiet Standing Balance During the Execution of ADLs

Author

Guardiola-Luna, Irene, Monasterio-Exposito, Leticia, Macias-Guarasa, Javier, Nieva-Suarez, Alvaro, Murillo-Teruel, Marina, Martin-Sanchez, Jose Luis, Palazuelos-Cagigas, Sira Elena, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Chen, Phoebe, Editorial Board Member, Cuzzocrea, Alfredo, Editorial Board Member, Du, Xiaoyong, Editorial Board Member, Kara, Orhun, Editorial Board Member, Liu, Ting, Editorial Board Member, Sivalingam, Krishna M., Editorial Board Member, Slezak, Dominik, Editorial Board Member, Washio, Takashi, Editorial Board Member, Yang, Xiaokang, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Iliadis, Lazaros, editor, Maglogiannis, Ilias, editor, Alonso, Serafin, editor, Jayne, Chrisina, editor, and Pimenidis, Elias, editor

Published
2023
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26. LoockMe: An Ever Evolving Artificial Intelligence Platform for Location Scouting in Greece

Author

Trivizakis, Eleftherios, Aidonis, Vassilios, Pezoulas, Vassilios C., Goletsis, Yorgos, Oikonomou, Nikolaos, Stefanis, Ioannis, Chondromatidou, Leoni, Fotiadis, Dimitrios I., Tsiknakis, Manolis, Marias, Kostas, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Chen, Phoebe, Editorial Board Member, Cuzzocrea, Alfredo, Editorial Board Member, Du, Xiaoyong, Editorial Board Member, Kara, Orhun, Editorial Board Member, Liu, Ting, Editorial Board Member, Sivalingam, Krishna M., Editorial Board Member, Slezak, Dominik, Editorial Board Member, Washio, Takashi, Editorial Board Member, Yang, Xiaokang, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Iliadis, Lazaros, editor, Maglogiannis, Ilias, editor, Alonso, Serafin, editor, Jayne, Chrisina, editor, and Pimenidis, Elias, editor

Published
2023
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27. Cross-Learning-Based Sales Forecasting Using Deep Learning via Partial Pooling from Multi-level Data

Author

Oliveira, José Manuel, Ramos, Patrícia, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Chen, Phoebe, Editorial Board Member, Cuzzocrea, Alfredo, Editorial Board Member, Du, Xiaoyong, Editorial Board Member, Kara, Orhun, Editorial Board Member, Liu, Ting, Editorial Board Member, Sivalingam, Krishna M., Editorial Board Member, Slezak, Dominik, Editorial Board Member, Washio, Takashi, Editorial Board Member, Yang, Xiaokang, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Iliadis, Lazaros, editor, Maglogiannis, Ilias, editor, Alonso, Serafin, editor, Jayne, Chrisina, editor, and Pimenidis, Elias, editor

Published
2023
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28. Gaussian-Based Approach for Out-of-Distribution Detection in Deep Learning

Author

Carvalho, Thiago, Vellasco, Marley, Amaral, José Franco, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Chen, Phoebe, Editorial Board Member, Cuzzocrea, Alfredo, Editorial Board Member, Du, Xiaoyong, Editorial Board Member, Kara, Orhun, Editorial Board Member, Liu, Ting, Editorial Board Member, Sivalingam, Krishna M., Editorial Board Member, Slezak, Dominik, Editorial Board Member, Washio, Takashi, Editorial Board Member, Yang, Xiaokang, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Iliadis, Lazaros, editor, Maglogiannis, Ilias, editor, Alonso, Serafin, editor, Jayne, Chrisina, editor, and Pimenidis, Elias, editor

Published
2023
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29. Discrimination of Attention Deficit Hyperactivity Disorder Using Capsule Networks and LSTM Networks on fMRI Data

Author

Dey, Arunav, Singh, Jigya, Rathore, Manaswini, Govind, Roshni, Ladwani, Vandana M., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Chen, Phoebe, Editorial Board Member, Cuzzocrea, Alfredo, Editorial Board Member, Du, Xiaoyong, Editorial Board Member, Kara, Orhun, Editorial Board Member, Liu, Ting, Editorial Board Member, Sivalingam, Krishna M., Editorial Board Member, Slezak, Dominik, Editorial Board Member, Washio, Takashi, Editorial Board Member, Yang, Xiaokang, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Iliadis, Lazaros, editor, Maglogiannis, Ilias, editor, Alonso, Serafin, editor, Jayne, Chrisina, editor, and Pimenidis, Elias, editor

Published
2023
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30. Advanced Skin Cancer Detection Using Deep Learning

Author

Alzamel, Mai, Alhejaili, Seba, Alhumaidhi, Fatimah, Alismail, Joud, Almubarak, Lama, Altammami, Halah, Iliopoulos, Costas, Lim, Zara, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Chen, Phoebe, Editorial Board Member, Cuzzocrea, Alfredo, Editorial Board Member, Du, Xiaoyong, Editorial Board Member, Kara, Orhun, Editorial Board Member, Liu, Ting, Editorial Board Member, Sivalingam, Krishna M., Editorial Board Member, Slezak, Dominik, Editorial Board Member, Washio, Takashi, Editorial Board Member, Yang, Xiaokang, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Iliadis, Lazaros, editor, Maglogiannis, Ilias, editor, Alonso, Serafin, editor, Jayne, Chrisina, editor, and Pimenidis, Elias, editor

Published
2023
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31. Classification of Time Signals Using Machine Learning Techniques

Author

Jadoon, Ishfaq Ahmad, Logofătu, Doina, Islam, Mohammad Nahin, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Chen, Phoebe, Editorial Board Member, Cuzzocrea, Alfredo, Editorial Board Member, Du, Xiaoyong, Editorial Board Member, Kara, Orhun, Editorial Board Member, Liu, Ting, Editorial Board Member, Sivalingam, Krishna M., Editorial Board Member, Slezak, Dominik, Editorial Board Member, Washio, Takashi, Editorial Board Member, Yang, Xiaokang, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Iliadis, Lazaros, editor, Maglogiannis, Ilias, editor, Alonso, Serafin, editor, Jayne, Chrisina, editor, and Pimenidis, Elias, editor

Published
2023
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32. Computational Ethology: Short Review of Current Sensors and Artificial Intelligence Based Methods

Author

Aguilar-Moreno, Marina, Graña, Manuel, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Chen, Phoebe, Editorial Board Member, Cuzzocrea, Alfredo, Editorial Board Member, Du, Xiaoyong, Editorial Board Member, Kara, Orhun, Editorial Board Member, Liu, Ting, Editorial Board Member, Sivalingam, Krishna M., Editorial Board Member, Slezak, Dominik, Editorial Board Member, Washio, Takashi, Editorial Board Member, Yang, Xiaokang, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Iliadis, Lazaros, editor, Maglogiannis, Ilias, editor, Alonso, Serafin, editor, Jayne, Chrisina, editor, and Pimenidis, Elias, editor

Published
2023
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34. Generative Model Watermarking Based on Human Visual System

Author

Zhang, Li, Liu, Yong, Liu, Shaoteng, Yang, Tianshu, Wang, Yexin, Zhang, Xinpeng, Wu, Hanzhou, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhai, Guangtao, editor, Zhou, Jun, editor, Yang, Hua, editor, Yang, Xiaokang, editor, An, Ping, editor, and Wang, Jia, editor

Published
2023
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35. A Cross-Modality Sketch Person Re-identification Model Based on Cross-Spectrum Image Generation

Author

Chen, Qingshan, Quan, Zhenzhen, Zhao, Kun, Zheng, Yifan, Liu, Zhi, Li, Yujun, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhai, Guangtao, editor, Zhou, Jun, editor, Yang, Hua, editor, An, Ping, editor, and Yang, Xiaokang, editor

Published
2022
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36. Global and Local Feature Based Deep Cross-Modal Hashing

Author

Yu, Haibo, Ma, Ran, Su, Min, An, Ping, Li, Kai, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhai, Guangtao, editor, Zhou, Jun, editor, Yang, Hua, editor, An, Ping, editor, and Yang, Xiaokang, editor

Published
2022
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37. A CNN-Based Quality Assessment Method for Pseudo 4K Contents

Author

Lu, Wei, Sun, Wei, Zhu, Wenhan, Min, Xiongkuo, Zhang, Zicheng, Wang, Tao, Zhai, Guangtao, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhai, Guangtao, editor, Zhou, Jun, editor, Yang, Hua, editor, An, Ping, editor, and Yang, Xiaokang, editor

Published
2022
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38. Application and Research of Deep Learning in Wheel Tread Defect Detection

Author

Wang, Jiaxin, Li, Hongtao, Liang, Zheng, Huang, Xiaoguang, Wang, Juan, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhai, Guangtao, editor, Zhou, Jun, editor, Yang, Hua, editor, An, Ping, editor, and Yang, Xiaokang, editor

Published
2022
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39. Efficient Violence Detection Using Transfer Learning

Author

Santos, Flávio, Durães, Dalila, Marcondes, Francisco S., Lange, Sascha, Machado, José, Novais, Paulo, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Chen, Phoebe, Editorial Board Member, Cuzzocrea, Alfredo, Editorial Board Member, Du, Xiaoyong, Editorial Board Member, Kara, Orhun, Editorial Board Member, Liu, Ting, Editorial Board Member, Sivalingam, Krishna M., Editorial Board Member, Slezak, Dominik, Editorial Board Member, Washio, Takashi, Editorial Board Member, Yang, Xiaokang, Editorial Board Member, Yuan, Junsong, Editorial Board Member, De La Prieta, Fernando, editor, El Bolock, Alia, editor, Durães, Dalila, editor, Carneiro, João, editor, Lopes, Fernando, editor, and Julian, Vicente, editor

Published
2021
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40. A Simple Strategy for Choosing Network Structures in a Object Detection Project with Transfer Learning

Author

Sartori, Laercio, Durães, Dalila, Novais, Paulo, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Chen, Phoebe, Editorial Board Member, Cuzzocrea, Alfredo, Editorial Board Member, Du, Xiaoyong, Editorial Board Member, Kara, Orhun, Editorial Board Member, Liu, Ting, Editorial Board Member, Sivalingam, Krishna M., Editorial Board Member, Slezak, Dominik, Editorial Board Member, Washio, Takashi, Editorial Board Member, Yang, Xiaokang, Editorial Board Member, Yuan, Junsong, Editorial Board Member, De La Prieta, Fernando, editor, El Bolock, Alia, editor, Durães, Dalila, editor, Carneiro, João, editor, Lopes, Fernando, editor, and Julian, Vicente, editor

Published
2021
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41. An Attentional Model for Earthquake Prediction Using Seismic Data

Author

de Santana Correia, Alana, Cleveston, Iury, dos Santos, Viviane Bonadia, Avila, Sandra, Colombini, Esther Luna, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Chen, Phoebe, Editorial Board Member, Cuzzocrea, Alfredo, Editorial Board Member, Du, Xiaoyong, Editorial Board Member, Kara, Orhun, Editorial Board Member, Liu, Ting, Editorial Board Member, Sivalingam, Krishna M., Editorial Board Member, Slezak, Dominik, Editorial Board Member, Washio, Takashi, Editorial Board Member, Yang, Xiaokang, Editorial Board Member, Yuan, Junsong, Editorial Board Member, De La Prieta, Fernando, editor, El Bolock, Alia, editor, Durães, Dalila, editor, Carneiro, João, editor, Lopes, Fernando, editor, and Julian, Vicente, editor

Published
2021
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42. Events-to-Frame: Bringing Visual Tracking Algorithm to Event Cameras

Author

Chan, Sixian, Liu, Qianqian, Zhou, XiaoLong, Bai, Cong, Chen, Nan, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhai, Guangtao, editor, Zhou, Jun, editor, Yang, Hua, editor, An, Ping, editor, and Yang, Xiaokang, editor

Published
2021
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43. Three-Dimensional Reconstruction of Intravascular Ultrasound Images Based on Deep Learning

Author

Cao, Yankun, Liu, Zhi, Xiao, Xiaoyan, Zheng, Yushuo, Cui, Lizhen, Du, Yixian, Zhang, Pengfei, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhai, Guangtao, editor, Zhou, Jun, editor, Yang, Hua, editor, An, Ping, editor, and Yang, Xiaokang, editor

Published
2020
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44. Intrusion Detection Based on Fusing Deep Neural Networks and Transfer Learning

Author

Xu, Yingying, Liu, Zhi, Li, Yanmiao, Zheng, Yushuo, Hou, Haixia, Gao, Mingcheng, Song, Yongsheng, Xin, Yang, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhai, Guangtao, editor, Zhou, Jun, editor, Yang, Hua, editor, An, Ping, editor, and Yang, Xiaokang, editor

Published
2020
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45. Hyperspectral Image Super-Resolution Using Multi-scale Feature Pyramid Network

Author

Sun, He, Zhong, Zhiwei, Zhai, Deming, Liu, Xianming, Jiang, Junjun, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhai, Guangtao, editor, Zhou, Jun, editor, Yang, Hua, editor, An, Ping, editor, and Yang, Xiaokang, editor

Published
2020
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46. Adaptive Person-Specific Appearance-Based Gaze Estimation

Author

Zheng, Chuanyang, Zhou, Jun, Sun, Jun, Zhao, Lihua, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhai, Guangtao, editor, Zhou, Jun, editor, Yang, Hua, editor, An, Ping, editor, and Yang, Xiaokang, editor

Published
2020
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47. Learn a Deep Convolutional Neural Network for Image Smoke Detection

Author

Liu, Maoshen, Gu, Ke, Wu, Li, Xu, Xin, Qiao, Junfei, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhai, Guangtao, editor, Zhou, Jun, editor, An, Ping, editor, and Yang, Xiaokang, editor

Published
2019
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48. Improving Semantic Style Transfer Using Guided Gram Matrices

Author

Nicolas, Chung, Xie, Rong, Song, Li, Zhang, Wenjun, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhai, Guangtao, editor, Zhou, Jun, editor, An, Ping, editor, and Yang, Xiaokang, editor

Published
2019
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49. Feature-Selecting Based Hashing via Deep Convolutional Neural Networks

Author

Zheng, Honghe, Ma, Ran, An, Ping, Li, Tong, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhai, Guangtao, editor, Zhou, Jun, editor, An, Ping, editor, and Yang, Xiaokang, editor

Published
2019
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50. A Deep Learning Based Perceptual Bit Allocation Scheme on Conversational Videos for HEVC -Domain Rate Control

Author

Zhu, Lisha, Wang, Guozhong, Teng, Guowei, Yang, Zhenglong, Zhang, Liliang, Barbosa, Simone Diniz Junqueira, Series editor, Chen, Phoebe, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Yuan, Junsong, Series editor, Zhou, Lizhu, Series editor, Zhai, Guangtao, editor, Zhou, Jun, editor, and Yang, Xiaokang, editor

Published
2018
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