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Pham, Tuan, ProfessorORCID iD iconorcid.org/0000-0002-4255-5130
Alternative names
Biography [eng]

I am a Professor of Biomedical Engineering.  My current research focuses on image processing, time-series analysis and pattern recognition applied to medicine, biology, and mental health.  

Publications (10 of 119) Show all publications
Pham, T. (2019). Deep learning of p73 biomarker expression in rectal cancer patients. In: : . Paper presented at 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, July 14-19, 2019. , Article ID N-19612.
Open this publication in new window or tab >>Deep learning of p73 biomarker expression in rectal cancer patients
2019 (English)Conference paper, Published paper (Refereed)
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-159037 (URN)
Conference
2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, July 14-19, 2019
Available from: 2019-07-21 Created: 2019-07-21 Last updated: 2019-09-05Bibliographically approved
Jin, Q., Meng, Z., Pham, T., Chen, Q., Wei, L. & Su, R. (2019). DUNet: A deformable network for retinal vessel segmentation. Knowledge-Based Systems, 178, 149-162
Open this publication in new window or tab >>DUNet: A deformable network for retinal vessel segmentation
Show others...
2019 (English)In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 178, p. 149-162Article in journal (Refereed) Published
Abstract [en]

Automatic segmentation of retinal vessels in fundus images plays an important role in the diagnosis of some diseases such as diabetes and hypertension. In this paper, we propose Deformable U-Net (DUNet), which exploits the retinal vessels’ local features with a U-shape architecture, in an end to end manner for retinal vessel segmentation. Inspired by the recently introduced deformable convolutional networks, we integrate the deformable convolution into the proposed network. The DUNet, with upsampling operators to increase the output resolution, is designed to extract context information and enable precise localization by combining low-level features with high-level ones. Furthermore, DUNet captures the retinal vessels at various shapes and scales by adaptively adjusting the receptive fields according to vessels’ scales and shapes. Public datasets: DRIVE, STARE, CHASE_DB1 and HRF are used to test our models. Detailed comparisons between the proposed network and the deformable neural network, U-Net are provided in our study. Results show that more detailed vessels can be extracted by DUNet and it exhibits state-of-the-art performance for retinal vessel segmentation with a global accuracy of 0.9566/0.9641/0.9610/0.9651 and AUC of 0.9802/0.9832/0.9804/0.9831 on DRIVE, STARE, CHASE_DB1 and HRF respectively. Moreover, to show the generalization ability of the DUNet, we use another two retinal vessel data sets, i.e., WIDE and SYNTHE, to qualitatively and quantitatively analyze and compare with other methods. Extensive cross-training evaluations are used to further assess the extendibility of DUNet. The proposed method has the potential to be applied to the early diagnosis of diseases.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Retinal blood vessel, Segmentation, DUNet, U-Net, Deformable convolution
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-157172 (URN)10.1016/j.knosys.2019.04.025 (DOI)000472687500013 ()2-s2.0-85065243868 (Scopus ID)
Note

Funding agencies: National Natural Science Foundation of China [61702361]; Science and Technology Program of Tianjin, China [16ZXHLGX00170]; National Key Technology R&D Program of China [2018YFB1701700]

Available from: 2019-06-01 Created: 2019-06-01 Last updated: 2019-07-19Bibliographically approved
Pham, T. (2019). Geostatistical Simulation of Medical Images for Data Augmentation in Deep Learning. IEEE Access, 7, 68752-68763
Open this publication in new window or tab >>Geostatistical Simulation of Medical Images for Data Augmentation in Deep Learning
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 68752-68763Article in journal (Refereed) Published
Abstract [en]

Data augmentation, which is the process for creating alternative copies of each sample in a small training data set, is important for extracting deep-learning features for medical image classification problems. However, data augmentation has not been well explored and existing methods are heuristic. In this paper, a geostatistical simulation of images is introduced as a data augmentation approach for extracting deep-learning features from medical images that are characterized with texture. The stochastic simulation procedure is to generate realizations of an image by modeling its spatial variability through the generation of multiple equiprobable stochastic realizations. The approach employed for the geostatistical simulation is based on the concepts of regionalized variables and kriging formulation to create multiple textural variations of medical images. Experimental results on classifying two medical-image data sets show that the use of geostatistical simulation for extracting deep-learning features with several popular pre-trained deep-learning models (AlexNet, ResNet-50, and GoogLeNet/Inception) provided better accuracy rates and more balanced results in terms of sensitivity and specificity than either with or without the implementation of conventional data augmentation. The proposed approach can be applied to other pre-trained networks as well as those without data pre-training for effective deep-feature extraction from medical images. The proposed application of the theory of geostatistics for medical image data augmentation in deep learning is original. The novelty of this approach can also be applied to many other types of data that are inherently textural. Geostatistical simulation opens a new door to the development of state-of-the-art artificial intelligence in feature extraction of medical images.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Keywords
Medical image features, sequential Gaussian simulation, geostatistics, deep learning, convolutional neural networks, classification.
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-157171 (URN)10.1109/ACCESS.2019.2919678 (DOI)000471349000001 ()
Available from: 2019-06-01 Created: 2019-06-01 Last updated: 2019-07-15Bibliographically approved
Pham, T. (2019). Quantification of white matter lesions on brain MRI with 2D fuzzy weighted recurrence networks. In: : . Paper presented at 9th International IEEE/EMBS Conference on Neural Engineering (NER'19), San Francisco, CA, USA, 20-23 March 2019 (pp. 110-113). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Quantification of white matter lesions on brain MRI with 2D fuzzy weighted recurrence networks
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

White matter lesions detected on magnetic resonance imaging scans of the brain have been hypothesized to have associations with the causes of several diseases. Accurate quantification of white matter lesions is important for the hypothesis validation. However, the clinical quantification is highly variable due to subjective opinions of different raters and is likely to compromise the reliability of the assessment. This paper introduces a new method of two-dimensional fuzzy weighted recurrence networks that can numerically express the quantity of white matter lesions. The results illustrate the promising application of the proposed method that offers as a useful computational tool in brain research.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-157173 (URN)10.1109/NER.2019.8717150 (DOI)000469933200028 ()9781538679210 (ISBN)9781538679227 (ISBN)
Conference
9th International IEEE/EMBS Conference on Neural Engineering (NER'19), San Francisco, CA, USA, 20-23 March 2019
Available from: 2019-06-01 Created: 2019-06-01 Last updated: 2019-07-03Bibliographically approved
Pham, T. (2019). Tensor decomposition of non-EEG physiological signals for visualization and recognition of human stress. In: : . Paper presented at 11th Int. Conf. Bioinformatics and Biomedical Technology, Stockholm, Sweden, May 29 - 31, 2019 (pp. 132-136). New York: ACM Publications
Open this publication in new window or tab >>Tensor decomposition of non-EEG physiological signals for visualization and recognition of human stress
2019 (English)Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
New York: ACM Publications, 2019
National Category
Other Medical Engineering Psychiatry
Identifiers
urn:nbn:se:liu:diva-159038 (URN)10.1145/3340074.3340096 (DOI)2-s2.0-85070558211 (Scopus ID)978-1-4503-6231-3 (ISBN)
Conference
11th Int. Conf. Bioinformatics and Biomedical Technology, Stockholm, Sweden, May 29 - 31, 2019
Available from: 2019-07-21 Created: 2019-07-21 Last updated: 2019-09-05Bibliographically approved
Cirillo, M. D., Mirdell, R., Sjöberg, F. & Pham, T. (2019). Time-Independent Prediction of Burn Depth using Deep Convolutional Neural Networks. Journal of Burn Care & Research
Open this publication in new window or tab >>Time-Independent Prediction of Burn Depth using Deep Convolutional Neural Networks
2019 (English)In: Journal of Burn Care & Research, ISSN 1559-047X, E-ISSN 1559-0488Article in journal (Refereed) In press
Abstract [en]

We present in this paper the application of deep convolutional neural networks, which are a state-of-the-art artificial intelligence (AI) approach in machine learning, for automated time-independent prediction of burn depth. Colour images of four types of burn depth injured in first few days, including normal skin and background, acquired by a TiVi camera were trained and tested with four pre-trained deep convolutional neural networks: VGG-16, GoogleNet, ResNet-50, and ResNet-101. In the end, the best 10-fold cross-validation results obtained from ResNet- 101 with an average, minimum, and maximum accuracy are 81.66%, 72.06% and 88.06%, respectively; and the average accuracy, sensitivity and specificity for the four different types of burn depth are 90.54%, 74.35% and 94.25%, respectively. The accuracy was compared to the clinical diagnosis obtained after the wound had healed. Hence, application of AI is very promising for prediction of burn depth and therefore can be a useful tool to help in guiding clinical decision and initial treatment of burn wounds.

Place, publisher, year, edition, pages
Oxford University Press, 2019
Keywords
Burn depth, time-independent prediction, deep convolutional neural network, artificial intelligence
National Category
Surgery Medical Image Processing Other Clinical Medicine
Identifiers
urn:nbn:se:liu:diva-157386 (URN)10.1093/jbcr/irz103 (DOI)31187119 (PubMedID)
Available from: 2019-06-11 Created: 2019-06-11 Last updated: 2019-06-27Bibliographically approved
Liu, J., Pham, T., Yan, H. & Liang, Z. (2018). Fuzzy mixed-prototype clustering algorithm for microarray data analysis. Neurocomputing, 276, 42-54
Open this publication in new window or tab >>Fuzzy mixed-prototype clustering algorithm for microarray data analysis
2018 (English)In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 276, p. 42-54Article in journal (Refereed) Published
Abstract [en]

Being motivated by combining the advantages of hyperplane-based pattern analysis and fuzzy clustering techniques, we present in this paper a fuzzy mix-prototype (FMP) clustering for microarray data analysis. By integrating spherical and hyper-planar cluster prototypes, the FMP is capable of capturing latent data models with both spherical and non-spherical geometric structures. Our contributions of the paper can be summarized into three folds: first, the objective function of the FMP is formulated. Second, an iterative solution which minimizes the objective function under given constraints is derived. Third, the effectiveness of the proposed FMP is demonstrated through experiments on yeast and leukemia data sets.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
FMP, Microarray data analysis, Fuzzy clustering
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-141163 (URN)10.1016/j.neucom.2017.06.083 (DOI)000419222000005 ()
Available from: 2017-09-25 Created: 2017-09-25 Last updated: 2018-01-22Bibliographically approved
Pham, T. (2018). Fuzzy Weighted Recurrence Networks of Time Series. Physica A: Statistical Mechanics and its Applications
Open this publication in new window or tab >>Fuzzy Weighted Recurrence Networks of Time Series
2018 (English)In: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371Article in journal (Refereed) Accepted
National Category
Other Medical Engineering
Identifiers
urn:nbn:se:liu:diva-150832 (URN)
Available from: 2018-09-03 Created: 2018-09-03 Last updated: 2018-09-05Bibliographically approved
Pham, T. (2018). Nonlinear dynamics analysis of short-time photoplethysmogram in Parkinson's disease. In: : . Paper presented at 2018 IEEE International Conference on Fuzzy Systems (pp. 1749-1754). IEEE
Open this publication in new window or tab >>Nonlinear dynamics analysis of short-time photoplethysmogram in Parkinson's disease
2018 (English)Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2018
National Category
Other Medical Engineering
Identifiers
urn:nbn:se:liu:diva-149772 (URN)978-1-5090-6020-7 (ISBN)
Conference
2018 IEEE International Conference on Fuzzy Systems
Available from: 2018-07-18 Created: 2018-07-18 Last updated: 2018-08-15
Pham, T. (2018). Pattern analysis and classification of blood oxygen saturation signals with nonlinear dynamics features. In: : . Paper presented at 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (pp. 112-115). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Pattern analysis and classification of blood oxygen saturation signals with nonlinear dynamics features
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Pattern analysis of blood oxygen saturation is important for gaining insights into the cardiorespiratory control system, real-time monitoring during operations, identifying potential predictors for the diagnosis of disease severity, and improving the hospitalization of patients with critical chronic diseases. This paper investigates the use of nonlinear dynamics features for machine learning and classification of blood oxygen saturation signals in healthy young and healthy old subjects. The validation of the feature reliability for the signal variability analysis has a clinical implication for differentiating blood oxygen saturation in patients with respect to the particular influence of aging, when patient's data become available.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
National Category
Other Medical Engineering
Identifiers
urn:nbn:se:liu:diva-147558 (URN)10.1109/BHI.2018.8333382 (DOI)978-1-5386-2405-0 (ISBN)978-1-5386-2406-7 (ISBN)
Conference
2018 IEEE EMBS International Conference on Biomedical & Health Informatics
Available from: 2018-04-26 Created: 2018-04-26 Last updated: 2018-05-18
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-4255-5130

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