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د.محسن بلال محمد فاروق

أستاذ مشارك كلية هندسة وعلوم الحاسب بالخرج

الخرج
011-588-8888
m.farooq@psau.edu.sa

السيرة الذاتية
الأبحاث العلمية

المؤهلات العلمية

الأبحاث العلمية

Journal Publications
1. Zamanitajeddin N., Jahanifar M., Bilal M., Eastwood M., Rajpoot N., Social network analysis of cell networks improves deep learning for prediction of molecular pathways and key mutations in colorectal cancer, Medical Image Analysis, Volume 93, 2024, 103071, https://doi.org/10.1016/j.media.2023.103071 [IF: 10.9]
2. Ibrahim A., Jahanifar M., Wahab N., Toss M., Makhlouf S., Atallah N., Lashen A., Katayama A., Graham S., Bilal M., et al. , Artificial Intelligence-Based Mitosis Scoring in Breast Cancer: Clinical Application, Modern Pathology, Vol. 37, No. 3, 2024, https://doi.org/10.1016/j.modpat.2023.100416. [IF: 7.5]
3. Lu, W., Lashen, A.G., Wahab, N., Miligy, I.M., Jahanifar, M., Toss, M., Graham, S., Bilal, M., Bhalerao, A., Atallah, N.M., Makhlouf, S., Ibrahim, A.Y., Snead, D., Minhas, F., Raza, S.E.A., Rakha, E. and Rajpoot, N. (2024), AI-based intra-tumor heterogeneity score of Ki67 expression as a prognostic marker for early-stage ER+/HER2− breast cancer. J Pathol Clin Res, Vol. 10, No. 1, e346. https://doi.org/10.1002/cjp2.346 [IF: 4.373]
4. Bilal M., Y.W. Tsang, M Ali, S. Graham, E. Hero, N. Wahab, K. Dodd, H. Sahota, S. Wu, W. Lu, et al.. Development and validation of AI-based pre-screening of large bowel biopsies; The Lancet Digital Health; Vol. 5, No. 11, E786-E797, November 2023. https://doi.org/10.1016/S2589-7500(23)00148-6 [IF: 30.8]
5. Wahab N., Toss M., Miligy I., Jahanifar M., Atallah N., Lu, W., Graham S., Bilal M., et al. AI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer. npj Precision Oncology. Vol. 7. No. 1. 2023. Accepted [IF: 7.9]
6. Gunesli, G., Bilal, M., Raza, S. et al. A Federated Learning Approach to Tumor Detection in Colon Histology Images. J Med Syst. Vol 47, No. 1. Pages. 99 (2023). https://doi.org/10.1007/s10916-023-01994-5 .[IF: 5.3]
7. Makhlouf, S., Wahab, N., Toss, M., …, Bilal, M., et al. Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence. Br J Cancer. Vol. 129. No. 11 (2023). https://doi.org/10.1038/s41416-023-02451-3. [IF: 9.082]
8. Bilal M., Jewsbury R., Wang R., AlGhamdi H.M., Asif A., Eastwood M., Rajpoot N., An aggregation of aggregation methods in computational pathology, Medical Image Analysis, Volume 88, 2023, 102885, ISSN 1361-8415, https://doi.org/10.1016/j.media.2023.102885. [IF: 10.9]
9. Atallah N.M., Wahab N., Toss M.S., Makhlouf S., Ibrahim A.Y., Lashen A.G., Ghannam S., Mongan N.P,, Jahanifar M., Graham S., Bilal M., et al., Deciphering the Morphology of Tumor-stromal Features in Invasive Breast Cancer Using Artificial Intelligence, Modern Pathology, 2023, 100254, ISSN 0893-3952, https://doi.org/10.1016/j.modpat.2023.100254. [IF: 7.5]
10. Graham S., Minhas F., Bilal M., et al Screening of normal endoscopic large bowel biopsies with interpretable graph learning: a retrospective study. Gut. Vol. 7, N0. 09, 2023. 709-1721. doi: 10.1136/gutjnl-2023-329512; [IF: 24.5]
11. Bilal, M., Nimir, M., Snead, D. et al. Role of AI and digital pathology for colorectal immuno-oncology. Br J Cancer 128, 3–11 (2023). https://doi.org/10.1038/s41416-022-01986-1. [IF: 9.082]
12. Pocock, J., Graham, S., Vu, Q.D. et al. TIAToolbox as an end-to-end library for advanced tissue image analytics. Commun Med 2, 120 (2022). https://doi.org/10.1038/s43856-022-00186-5
13. Wahab N, Miligy I., Dodd k., Sahota H., Toss M., Lu W., Jahanifar M., Bilal M., et al. “Semantic annotation for computational pathology: multidisciplinary experience and best practice recommendations, J Pathol Clin Res, 8: 116-128. 2022. [IF: 4.373]
14. Bilal M., S.A. Raza, A. Azam, S. Graham, M. Ilyas, I.A. Cree, D. Snead, F. Minhas, N. M. Rajpoot, “Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study”, The Lancet Digital Health; Vol. 3, No. 12, E763-E772, Dec 01, 2021. [IF: 30.8]
15. Bilal, M., M. Arif, “A Generalized Modeling of ill-posed Inverse Reconstruction of Images by a Novel Data-driven Framework,” Signal, Image, and Video Processing, SIViP 14, 333–341 (2020). [IF: 2.3]
16. Bilal, M., F.K. Shaikh, M. Arif, M.F. Wyne, “A revised framework of machine learning application for optimal activity recognition,” Cluster Computing 22, 7257–7273 (2019). [IF: 4.4]
17. Bilal, M., Mujtaba, H., and Jaffar, M. A., “Modified particle swarm optimization and fuzzy regularization for pseudo de-convolution of spatially variant blurs,” Multimedia Tools and Applications, published online 22 Apr. 2015; Vol. 75, No. 11, 2016, pp. 6533–6548. [IF: 3.6]
18. Bilal, M. Mujtaba, H., and Jaffar, M. A., “Novel Optimization Framework to Recover True Image Data,” Cognitive Computations, published online 30 Jun. 2015; Vol. 7, No. 6, 2015, pp. 680–692. [IF: 5.4]
19. Bilal, M., Jaffar, M. M., Hussain, A., and Shim, S. O., “Optimal edge preserving restoration with efficient regularisation,” The Imaging Science Journal 27 Jun. 2014; Vol. 63, No. 2, 2015, pp. 86–75. [CD2 (HJRS-HEC): 1.17, IF: 0.871]
20. Arif, M., Bilal, M., Kattan, A., and Ahamed, S. I., “Better Physical Activity Classification Using Smartphone Acceleration Sensor,” J Med Syst, published online 8 Jul. 2014; Vol. 38, No. 95, 2014, pp. 1–10. [IF: 5.3]
21. Bilal, M., Hussain, A., Jaffar, M. A., Choi, T. S., and Mirza, A. M., “Estimation and optimization based ill-posed inverse restoration using fuzzy logic,” Multimedia Tools and Applications, published online 06 Jul. 2012; Vol. 69, No. 3, 2014, pp. 1067–1087. [IF: 3.6]
22. Bilal, M., Shams-ur-Rehman, M., and Jaffar, M. A., “Evolutionary Reconstruction: Image Restoration for Space Variant Degradation,” Smart Computing Review [Online], Vol. 3, No. 4, 2013, pp. 220–232.
23. Khan, S., Bilal, M., Sharif M., and Baig, R., “Ant n-Queen Solver,” International Journal of Artificial Intelligence [Online], Vol. 7, No. A11, 2011. [CD2 (HJRS-HEC): 3.81]
Preprints
24. Bilal M., Tsnag Y.W., et al. AI based pre-screening of large bowel cancer via weakly supervised learning of colorectal biopsy histology images, medRxiv 2022.02.28.22271565,
https://doi.org/10.1101/2022.02.28.22271565
25. Awan R., M. Nimir, S. Raza, M. Bilal, J. Lotz, D. Snead, A. Robison, N. Rajpoot. “Deep Learning based Prediction of MSI in Colorectal Cancer via Prediction of the Status of MMR Markers”, arXiv:2203.00449 (2022); https://doi.org/10.48550/arXiv.2203.00449
26. Hatami N., M. Bilal and N. Rajpoot. “Deep Multi-Resolution Dictionary Learning for Histopathology Image Analysis.” ArXiv abs/2104.00669 (2021); https://doi.org/10.48550/arXiv.2104.00669
Conference Proceedings
1. A. Saeed, H. Atif, U. Habib, M. Bilal. Remote Sensing Aircraft Classification Harnessing Deep Learning Advancements; 18th IEEE International Conference on Emerging Technologies (ICET 2023), FAST-NUCES, Peshwar, Pakistan; November 2023.
2. Jahanifar M., A. Shepard, N. Zamanitajeddin, R. M. S. Bashir, M. Bilal et al., (2022) Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge. In: Aubreville M., Zimmerer D., Heinrich M. (eds) Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis. MICCAI 2021. LNCS, vol 13166. Springer, Cham. https://doi.org/10.1007/978-3-030-97281-3_6
3. Wenqi Lu, Simon Graham, M. Bilal, Nasir Rajpoot, Fayyaz Minhas; Capturing Cellular Topology in Multi-Gigapixel Pathology Images, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Work-shops, 2020, pp. 260-261
4. T. Alkarakatly, S. Eidhah, M. Al-Sarawani, A. Al-Sobhi and M. Bilal, "Skin Lesions Identification Using Deep Convolutional Neural Network," 2019 International Conference on Advances in the Emerging Computing Technologies (AECT), Al Madinah Al Munawwarah, Saudi Arabia, 2020, pp. 1-5, doi: 10.1109/AECT47998.2020.9194205.
5. N Hatami, M Bilal, N Rajpoot, Deep Dictionary Learning for Colorectal Cancer Grading, ICLR 2020
6. Bilal, M., M. Arif, Multivariate Regression CNN for ill-posed Inverse Reconstruction of Satellite Images, in Elsevier 16th International Learning and Technology Conference, March 2019, Jed., KSA.
7. Bilal, M., Wyne, M. F. and Jaffar, M. A., “Image restoration by multivariate-stochastic optimization using improved particle swarm algorithm,” 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, 2016, pp. 2596-2603.
8. Khan, S., Bilal, M., Sharif, M., and Khan, F. A., “A Solution to Bipartite Drawing Problem Using Genetic Algorithm,” In: Tan Y., Shi Y., Chai Y., Wang G. (eds) Advances in Swarm Intelligence - Second International Conference, ICSI 2011, Lecture Notes in Computer Science, Vol. 6728, Springer, Berlin, Heidelberg.
9. Bilal, M., Sharif, M., Jaffar, M. A., Hussain, A., and Mirza, A. M., “Image Restoration Using Modified Hopfield Fuzzy Regularization Method,” 2010 5th International Conference on Future Information Technology, Busan, 2010, pp. 1-6.
10. Sharif, M., Bilal, M., Khan, S., and Jaffar, M.A., “Adaptive Filter and Morphological Operators Using Binary PSO,” In: Zhu R., Zhang Y., Liu B., Liu C. (eds) Information Computing and Applications. ICICA 2010. Lecture Notes in Computer Science, Vol. 6377, Springer, Berlin, Heidelberg.
11. Khan, S., Bilal, M., Sharif, M., Abbas, M. S., and Baig, R., “Solution of n-Queen problem using ACO,” 2009 IEEE 13th International Multitopic Conference, Islamabad, 2009, pp. 1-5.

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