Published in Scientific Data, 1900
We initially present a shell dataset, containing 7894 shell species with 29622 samples, where totally 59244 shell images for shell features extraction and recognition are used.
Recommended citation: Qi Zhang, Jianhang Zhou, Jing He, Xiaodong Cun, Shaoning Zeng & Bob Zhang (2019). "A shell dataset, for shell features extraction and recognition," Scientific Data. 226.
Published in Journal of NanJing Normal University, 1900
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Jianhang Zhou, Xubing Yang*, Fuquan Zhang, Qiaolin Ye, Dengping Xu. (2018). "Localized GEPSVM in Mahalanobis metric." Journal of NanJing Normal University, 41(65).
Published in Applied Sciences, 1900
We proposed TongueNet, which is a precise and fast automatic tongue segmentation system.
Recommended citation: Jianhang Zhou, Qi Zhang, Bob Zhang, Xiaojiao Chen (2019). "TongueNet: A Precise and Fast Tongue Segmentation System Using U-Net with a Morphological Processing Layer." Applied Sciences. 9(15), 3128.
Published in IEEE Transactions on Artificial Intelligence, 1900
we apply unified GR techniques and GCNs in a framework that can be implemented in semisupervised learning problems. To achieve this framework, we propose sparsity-induced graph convolutional network (SIGCN) for semisupervised learning. SIGCN introduces the sparsity to formulate significant relationships between instances by constructing a newly proposed $L_0$ -based graph (termed as the sparsity-induced graph) before applying graph convolution to capture the high-quality features based on this graph for label propagation.
Recommended citation: Zhou, J., Zeng, S., & Zhang, B. (2021). Sparsity-Induced Graph Convolutional Network for Semisupervised Learning. IEEE Transactions on Artificial Intelligence, 2(6), 549-563.
Published in Sensors, 1900
we propose a novel collaborative representation by directly using non-negative representations to represent a test sample collaboratively, termed Non-negative Collaborative Representation-based Classifier (NCRC).
Recommended citation: Jianhang Zhou, Bob Zhang (2019). "Collaborative Representation Using Non-Negative Samples for Image Classification." Sensors. 19(11), 2609.
Published in Expert Systems with Applications, 1900
We propose salient self-representation (R) that learns the salient information. A R-based classifier was specially designed for pattern classification. We prove the rationale of using salient information for pattern classification.
Recommended citation: Zhou, J., Zeng, S., & Zhang, B. (2023). Learning salient self-representation for image recognition via orthogonal transformation. Expert Systems with Applications, 212, 118663.
Published in Applied Intelligence, 1900
We proposed a Kernel Non-negative Representation-based Classifier (KNRC) for addressing this problem to achieve better results in pattern classification. Furthermore, we extended the KNRC to a dimensionality reduction version to reduce the dimensions of the KNRC’s feature space as well as improve its classification ability.
Recommended citation: Zhou, J.<\b>, Zeng, S., & Zhang, B. (2022). Kernel nonnegative representation-based classifier. Applied Intelligence, 52(2), 2269-2289. </p> </article> </div> Published in IEEE Journal of Biomedical and Health Informatics, 1900 We propose a graph based multichannel feature fusion (GBMFF) method to utilize the multichannel features of the wrist pulse signals effectively. Recommended citation: Zhang, Q., Zhou, J.<\b>, & Zhang, B. (2020). Graph based multichannel feature fusion for wrist pulse diagnosis. IEEE Journal of Biomedical and Health Informatics, 25(10), 3732-3743. </p> </article> </div> Published in International Joint Conference of Biometrics (IJCB2020), 1900 We initially present a shell dataset, containing 7894 shell species with 29622 samples, where totally 59244 shell images for shell features extraction and recognition are used. Recommended citation: Jianhang Zhou, Qi Zhang, Bob Zhang (2019). "A Progressive Stack Face-based Network for Detecting Diabetes Mellitus and Breast Cancer," International Joint Conference of Biometrics (IJCB2020). https://ieee-biometrics.org/ijcb2020/Program.html Published in ICASSP2020, 1900 The aim of this paper is to distinguish patients with diabetes mellitus, lung cancer from healthy people simultaneously by analyzing facial images through the stacked sparse autoencoder. Recommended citation: Qi Zhang, Jianhang Zhou, Bob Zhang (2020). "A Noninvasive Method to Detect Diabetes Mellitus and Lung Cancer Using the Stacked Sparse autoencoder." 2020 International Conference on Acoustics, Speech, and Signal Processing (ICASSP2020), 1409-1413. Published in IEEE Transactions on Neural Networks and Learning Systems, 1900 The FGSCN performs GC on the fuzzy subspace (F-space), which simultaneously learns from the underlying subspace information in the low-dimensional space as well as its neighborliness information in the high-dimensional space. Recommended citation: Zhou, J., Zhang, Q., Zeng, S., & Zhang, B. (2022). Fuzzy Graph Subspace Convolutional Network. IEEE Transactions on Neural Networks and Learning Systems. Published in Future Generation Computer Systems, 1900 We present an automatic multi-view disease detection system, which contains a series of automatic procedures. Recommended citation: Jianhang Zhou, Qi Zhang, Bob Zhang (2021). "An automatic multi-view disease detection system via Collective Deep Region-based Feature Representation." Future Generation Computer Systems. 107, 59-75. Published in Information Sciences, 1900 The two-stage strategy has been widely used in image classification. However, these methods barely take the classification criteria of the first stage into consideration in the second prediction stage. In this paper, we propose a novel Two-Stage Representation method (TSR), and convert it to a Single-Teacher SingleStudent (STSS) problem in our two-stage knowledge transfer framework for image classification. Recommended citation: Qi Zhang, Jianhang Zhou, Bob Zhang (2020). "DsNet: Dual stack network for detecting diabetes mellitus and chronic kidney disease." Information Sciences, 547, 945-962. Published in 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology., 1900 In this paper, we propose a novel classifier based on the fusion of the linear discriminant analysis (LDA) and the sparse representation based classifier (SRC) named L-SRC, to perform disease detection. Recommended citation: Jianhang Zhou, Q. Zhang, Bob Zhang*. (2019). "Applying L-SRC for non-invasive disease detection using facial chromaticity and texture features." 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology. (2019). Published in Computers in Biology and Medicine, 1900 We propose a two-phase sublingual-based disease detection framework for effective non-invasive multi-disease detection. Recommended citation: Zhou, J.<\b>, Zhang, Q., & Zhang, B. (2021). Two-phase non-invasive multi-disease detection via sublingual region. Computers in Biology and Medicine, 137, 104782. </p> </article> </div> Published in Computers in Biology and Medicine, 1900 We systematically summarize the development of computational TCM diagnosis. Recommended citation: Zhang, Q., Zhou, J.<\b>, & Zhang, B. (2021). Computational traditional Chinese medicine diagnosis: a literature survey. Computers in Biology and Medicine, 133, 104358. </p> </article> </div> Published in IEEE Access, 1900 The two-stage strategy has been widely used in image classification. However, these methods barely take the classification criteria of the first stage into consideration in the second prediction stage. In this paper, we propose a novel Two-Stage Representation method (TSR), and convert it to a Single-Teacher SingleStudent (STSS) problem in our two-stage knowledge transfer framework for image classification. Recommended citation: Jianhang Zhou, Shaoning Zeng, Bob Zhang (2020). "Linear Representation-Based Methods for Image Classification: A Survey." IEEE Access, vol.8, pp.216645-216670. Published in 30th British Machine Vision Conference (BMVC2019), 1900 In this paper, we propose a novel two-stage representation method (TSR), and convert it to a Single-Teacher Single-Student (STSS) problem in our two-stage image classification framework. Recommended citation: Jianhang Zhou, Shaoning Zeng, Bob Zhang (2019). "Two-stage Image Classification Supervised by a Single Teacher Single Student Model." 30th British Machine Vision Conference (BMVC2019), 0155. Published in IEEE Access, 1900 In this work, we propose a novel method for automatic segmentation and quantification of epicardial fat from CT scans accurately. Recommended citation: Qi Zhang, Jianhang Zhou, Bob Zhang, Weijia Jia, Enhua Wu (2020). "Automatic Epicardial Fat Segmentation and Quantification of CT Scans Using Dual U-Nets With a Morphological Processing Layer." IEEE Access, 8, 128032-128041. Published in Pattern Recognition, 1900 The two-stage strategy has been widely used in image classification. However, these methods barely take the classification criteria of the first stage into consideration in the second prediction stage. In this paper, we propose a novel Two-Stage Representation method (TSR), and convert it to a Single-Teacher SingleStudent (STSS) problem in our two-stage knowledge transfer framework for image classification. Recommended citation: Jianhang Zhou, Shaoning Zeng, Bob Zhang (2020). "Two-stage knowledge transfer framework for image classification." Pattern Recognition. 107(107529). Published in Artificial Intelligence in Medicine, 1900 We proposed a multi-feature representation method for detecting burn depth. Recommended citation: Zhang, B., & Zhou, J.<\b> (2021). Multi-feature representation for burn depth classification via burn images. Artificial Intelligence in Medicine, 118, 102128. </p> </article> </div> Graph based multichannel feature fusion for wrist pulse diagnosis
A Progressive Stack Face-based Network for Detecting Diabetes Mellitus and Breast Cancer
A Noninvasive Method to Detect Diabetes Mellitus and Lung Cancer Using the Stacked Sparse autoencoder
Fuzzy Graph Subspace Convolutional Network
An automatic multi-view disease detection system via Collective Deep Region-based Feature Representation
DsNet: Dual stack network for detecting diabetes mellitus and chronic kidney disease
Applying L-SRC for non-invasive disease detection using facial chromaticity and texture features
Two-phase non-invasive multi-disease detection via sublingual region
Computational traditional Chinese medicine diagnosis: a literature survey
Linear Representation-Based Methods for Image Classification: A Survey
Two-stage Image Classification Supervised by a Single Teacher Single Student Model
Automatic Epicardial Fat Segmentation and Quantification of CT Scans Using Dual U-Nets With a Morphological Processing Layer
Two-stage knowledge transfer framework for image classification
Multi-feature representation for burn depth classification via burn images