Muhammad Shaban

Thorn Building · 45 Francis Street · Boston, MA 02115 mshaban[at]

I am a research scientist with over seven years of research experience in computer vision, deep learning, and AI-driven medical image analysis. My current research focuses on the broad development of computer vision and deep learning algorithms for clinical translation in computational pathology.


Machine Learning Scientist

AI for Pathology Image Analysis Lab, Department of Pathology, Mass General Brigham, Harvard Medical School
  • Working on self-supervised representation learning from multiplex images.
November 2023 - Present

Postdoctoral Research Fellow

AI for Pathology Image Analysis Lab, Department of Pathology, Mass General Brigham, Harvard Medical School
  • 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.
February 2021 - October 2023

Research Assistant

Kindi Lab, Department of Computer Science, Qatar University
  • 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.
October 2015 - September 2016

Software Engineer

Techlogix Inc., Multinational IT Services, Consulting, and Business Solutions Company
  • Worked on Business Process Modeling (BPM) web application tools for Fortune 100 companies to streamline their business processes and enhance their productivity and efficiency manifold
  • Led Business Intelligence (BI) projects using Oracle BI and reporting Tools, which provided hands-on experience with large amounts of data and how to make it understandable, presentable, and usable for a novice user.
July 2011 - August 2013


University of Warwick, Coventry, UK

PhD in Computer Science, Department of Computer Science
  • 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
November 2016 - December 2020

Lahore University of Management Sciences, Lahore, PK

MS in Computer Science, School of Science and Engineering

Ranked 2nd in class of 40

August 2013 - June 2015

Punjab University, Lahore, PK

BS in Computer Science, College of Information Technology

Ranked 2nd in class of 45

October 2007 - July 2011

Selected Publications


  1. M Shaban, Y Bai, H Qiu, S Mao, J Yeung, YY Yeo, V Shanmugam, H Chen, B Zhu, GP Nolan, MA Shipp. MAPS: Pathologist-level cell type annotation from tissue images through machine learning. Accepted in Nature Communications, 2023.

  2. 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, 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.

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

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

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

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


Programming Languages & Tools

  • Python
  • Pytorch
  • Tensorflow
  • Keras

Awards & Certifications

  • Deep Learning Specialization - DeepLearning.AI Certification
  • Dean’s Honor Roll: Lahore University of Management Sciences, Lahore, PK
  • 2 nd Position: NUST ICT Commpec’11 Project Competition (FYP)
  • 2 nd Position: UET Techno FEST’11 Project Competition (FYP)
  • 3 rd Position: Code Spree 2011 (Intra Lahore Speed Programming Competition)