Publications
Journals:
Pham, Chi-Hieu, Saïd Ladjal, and Alasdair Newson. "PCA-AE: Principal Component Analysis Autoencoder for Organising the Latent Space of Generative Networks." Journal of Mathematical Imaging and Vision 64, no. 5 (2022): 569-585. [[Preprint 1, Preprint 2, Paper ,Original Code, Code using DeZero ]
Delannoy, Q., Pham, C. H., Cazorla, C., Tor-Díez, C., Dollé, G., Meunier, H., ... & Rousseau, F. (2020). SegSRGAN: Super-resolution and segmentation using generative adversarial networks—Application to neonatal brain MRI. Computers in Biology and Medicine, 103755. [Paper , Python package]
Pham, C. H. Tor-Díez, C., Meunier, H., Bednarek, N., Fablet, R., Passat, N., Rousseau, F. (2019). Multiscale brain MRI super-resolution using deep 3D convolutional networks. Computerized Medical Imaging and Graphics, 77, 101647. [Paper, Code]
Conferences:
Pham, C. H., Díez, C. T., Meunier, H., Bednarek, N., Fablet, R., Passat, N., Rousseau, F. (2019). Simultaneous super-resolution and segmentation using a generative adversarial network: Application to neonatal brain MRI. In 2019 IEEE 16th International Symposium on Biomedical Imaging. [Paper, Code]
Díez, C. T., Pham, C. H., Meunier, H., Faisan, S., Bloch, I., Bednarek, N., R., Passat, N., Rousseau, F. (2019). Evaluation of cortical segmentation pipelines on clinical neonatal MRI data. In 41st International Engineering in Medicine and Biology Conference.
Pham, C. H., Ducournau, A., Fablet, R., & Rousseau, F. (2017, April). Brain MRI super-resolution using deep 3D convolutional networks. In Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on (pp. 197-200). IEEE. [Paper, Code]
Le-Tien, T., & Pham-Chi, H. (2014). An Approach for Efficient Detection of Cephalometric Landmarks. Procedia Computer Science, 37, 293-300.
Thesis: