Prof Mourad Gridach

I am doing research in the field of artificial intelligence and machine learning. My goal is to use and develop models that can understand images, with focus on medical images.

I am focusing on developing deep neural networks models for segmenting various types of medical images such as computerized tomography, MRI, X-ray, etc.

As an approach, I am using deep neural networks, which are machine learning models giving state-of-the-art performances in fields like computer vision, natural language processing, speech recognition, etc.

 I also do research in the field of natural language processing and recommender systems, where I develop machine learning models to understand human language and recommend products. An example of these applications such as sentiment analysis, named entity recognition, churn prediction, part-of-speech tagging (POS), biomedical NLP, I focused on different languages such as English, French, Standard Arabic and Arabic Dialects.

Mourad was an AfOx-Collaborative Fellow in 2019.

During his 8 week AfOx fellowship in 2019, Mourad was hosted by the Department of Computer Science and Wolfson College.

1.  M. Gridach and I. Voiculescu. DOPNet: Densely Oriented Pooling Network for Medical Image Segmentation. Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI), Nice, France 2021.
2.  M. Gridach. PyDiNet: Pyramid Dilated Network for Medical Image Segmentation. Neural Networks. 2021. (Impact Factor: 7.309).
3.  Ali, S., Dmitrieva, M., Ghatwary, N., Bano, S., Polat, G., Gridach M., Temizel, A., Krenzer, A., Hekalo, A., Guo, Y.B., Matuszewski, B. et al.,., 2020. Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy. Medical Image Analysis. (Impact Factor: 11.148). 2021 Feb 17:102002.
4. M. Gridach and I. Voiculescu. OxEndoNET: A Dilated Convolutional Neural Networks for Endoscopic Artefact Segmentation. Proceedings of the IEEE International Symposium on Biomedical Imaging Workshop, EndoCV 2020.
5.  M. Gridach. Hybrid Deep Neural Networks for Recommender Systems. Neurocomputing. 2020. (Impact Factor: 4.438).