- To develop computer vision-based techniques for detecting and segmentation tissue structures in digital histology images to better understand and quantifysegmentation of tissue structures in digital histology images, to better understand and quantifyunderstanding and quantification of tumor micro-environment.
- To develop novel AI-based techniques for the discovery of new diagnostic, prognostic, and predictive digital biomarkers. These biomarkers are based on tissue morphology and architecture, tumor-immune microenvironment, and spatial signatures of a combination of the tumor, peri-tumor, and even some of the non-tumor regions in digital pathology images.
- To develop AI-based processing pipelines for histological and medical data with the aim to predict the overall survival, specific survival, event-free survival, or recurrence-free survival of patients. This can eventually lead to the development of tailor-made preventive diagnostics, therapeutics, and disease management strategies based on an individual’s omics profiles in a “big data” approach by exploiting the complex relationships and hidden patterns in the available “data”, of histological images as well as in genomics. The “big data” is analyzed for estimating pre-malignancy diagnosis, quantifying therapy response, disease stage, survival prediction, and precision medicine techniques.
- Sajid Javed, A. Mahmood, M. M. Fraz, , NA Koohbanani, K. Benes, Yee-Wah Tsang, K. Hewitt, D, Epstein, D. Snead, NM Rajpoot , “Cellular community detection for tissue phenotyping in colorectal cancer histology images”, Medical Image Analysis, Vol. 63 , No. 1, Jul, 2020. IF: 8.88
- M. Shaban, R. Awan, M.M. Fraz , A. Azam, Y. Tsang, D. Snead, N.M. Rajpoot , “Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images”, IEEE Transactions on Medical Imaging, Vol. 21 , No. 1, Feb, 2020. IF: 7.81
- M.M. Fraz , S. A. Khurran, S. Graham, M. Shaban, Asif Loya, N.M. Rajpoot , “FABnet: Feature attention based network for simultaneous segmentation of microvessels and nerves in routine histology images of oral cancer”, Neural Computing and Applications, Vol. 20 , No. 1, Nov, 2019. IF: 4.66
- M. Shaban, S.A. Khurram, M. M. Fraz , N. Alsubaie, I. Masood, S. Mushtaq, M. Hassan, A. Loya & N. M. Rajpoot , “A Novel Digital Score for Abundance of Tumour Infiltrating Lymphocytes Predicts Disease Free Survival in Oral Squamous Cell Carcinoma”, Nature Scientific Reports, Vol. 9 , No. 1, PP. 13341 (2019) , Sep, 2019. IF: 4.525
- R.M.S. Bashir, H. Mahmood, M. Shaban, SEA Raza, M. M. Fraz, , S.A. Khurram, N. M. Rajpoot , “Automated grade classification of oral epithelial dysplasia using morphometric analysis of histology images”, Proceedings of the Medical Imaging 2020: Digital Pathology, Feb, 2020, Houston, Texas , USA.
- M.M.Fraz, M.Shaban, S.Graham, S.A.Khurram, N.M.Rajpoot , , “Uncertainty Driven Pooling Network for Microvessel Segmentation in Routine Histology Images”, Proceedings of COMPAY workshop in 21st International Conference on Medical Image Computing and Computer Assisted Intervention, Sep, 2018, Granada , Spain.
- Sajid Javed, M.M. Fraz, David Epstein, David Snead, and Nasir M. Rajpoot , , “Cellular Community Detection for Tissue Classification”, Proceedings of COMPAY workshop in 21st International Conference on Medical Image Computing and Computer Assisted Intervention, Sep, 2018, Granada, Spain.