Muhammad Shaban

Machine Learning Scientist

• Working on self-supervised representation learning from multiplex spatial proteomics images.

Postdoctoral Research Fellow

• Worked on multiplex image analysis projects, including cell phenotyping, and protein marker imputation and digital biomarker identification for classical Hodgkin lymphoma progression.
• Worked on multimodal deep learning for fusing medical images and genomics for understanding cancer metastases and cancers of unknown primary
• Collaborated with labmates on different projects, including context-aware cancer prognosis using graph convolutional networks.

Research Assistant

• Participated in the CAMELYON16 consortium that demonstrated the first breakthrough using deep learning for computational pathology in lymph node metastasis classification, published in JAMA.
• Developed computer vision approaches for person pose detection via deep learning, published in Information Fusion.

Journals

1. M Shaban, DFK Williamson, MY Lu, RJ Chen, J Lipkova, TY Chen, and F Mahmood Deep learning-based multimodal integration of histology and genomics improves cancer origin prediction Accepted in Nature Biomedical Engineering, 2024.

2. M Shaban, Y Bai, H Qiu, S Mao, J Yeung, YY Yeo, V Shanmugam, H Chen, B Zhu, GP Nolan, MA Shipp, SJ Rodig, S Jiang, and F Mahmood MAPS: Pathologist-level cell type annotation from tissue images through machine learning. Nature Communications, 2024.

3. M Shaban, SEA Raza, M Hassan, A Jamshed, S Mushtaq, A Loya, N Batis, J Brooks, P Nankivell, N Sharma, M Robinson, H Mehanna, SA Khurram, and NM Rajpoot. A digital score of tumour-associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma. The Journal of Pathology, 2021.

4. M Shaban, R Awan, MM Fraz, A Azam, YW Tsang, D Snead, and NM Rajpoot. Context-aware convolutional neural network for grading of colorectal cancer histology images. IEEE Transactions on Medical Imaging (TMI), 2020.

5. M Shaban, SA Khurram, MM Fraz, N Alsubaie, I Masood, S Mushtaq, M Hassan, A Loya, and NM Rajpoot. A Novel Digital Score for Abundance of Tumour Infiltrating Lymphocytes Predicts Disease Free Survival in Oral Squamous Cell Carcinoma. Scientific Reports, 2019.

6. M Shaban, A Mahmood, SA Al-Maadeed, and NM Rajpoot. An information fusion framework for person localization via body pose in spectator crowds. Information Fusion, 2019.

Note: Check google scholar profile for updated and complete list of publications.

University of Warwick, Coventry, UK

• Proposed novel method for context-aware deep learning to leverage domain specific information in gigapixel histopathology slides for colorectal cancer grading.
• Developed two automated and objective methods for the quantification of histopathologic biomarkers in tumor microenvironment for better patient prognosis.
• Led teams of four or more researchers, including pathologists, in three different projects and successfully concluded those projects with journal publications.
• Actively participated in computational pathology challenges and presented work at several medical conferences.
• Published more than 15 publications in a span of four years.
• Worked under the supervision of Prof. Nasir Rajpoot in the Tissue Image Analytics (TIA) Center

Lahore University of Management Sciences, Lahore, PK