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Selected publications

Peer-reviewed publications


  • Marcolini A, Bussola N, Arbitrio E, Amgad M, Jurman G, Furlanello C. Histolab: A Python Library for Reproducible Digital Pathology Preprocessing with Automated Testing. SoftwareX . 2022 Jul;20: 101237. doi:10.1016/j.softx.2022.101237.

  • Elfer K, Dudgeon S, ..., Amgad M, ..., Gallas BD. Pilot study to evaluate tools to collect pathologist annotations for validating machine learning algorithms. Journal of Medical Imaging. 2022 Jul;9(4):047501. doi: 10.1117/1.JMI.9.4.047501.

  • Amgad M, Atteya LA, ..., Cooper LAD. NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer. GigaScience. 2022. doi: 10.1093/gigascience/giac037.


  • Amgad M, Atteya L, Hussein H, ..., Cooper LA. Explainable nucleus classification using Decision Tree Approximation of Learned Embeddings. Bioinformatics. 2021. doi: 10.1093/bioinformatics/btab670.

  • López-Pérez M, Amgad M, ..., Katsaggelos AK. Learning from crowds in digital pathology using scalable variational Gaussian processes. Scientific Reports. 2021;11(1):1-9. doi: 10.1038/s41598-021-90821-3.

  • Farris AB, Vizcarra J, Amgad M, ..., Hogan J. Artificial Intelligence and Algorithmic Computational Pathology: Introduction with Renal Allograft Examples. Histopathology. 2021. doi: 10.1111/his.14304

  • Farris AB, Vizcarra J, Amgad M, ..., Hogan J. Image Analysis Pipeline for Renal Allograft Evaluation and Fibrosis Quantification. Kidney International Reports. 2021. doi: 10.1016/j.ekir.2021.04.019.

  • Dudgeon SN, Wen S, ..., Amgad M, ..., Gallas BD. A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study. J Pathol Inform. 2021;12:45. doi: 10.4103/jpi.jpi_83_20.


  • Lee S, Amgad M, ..., Cooper LAD. Interactive classification of whole-slide imaging data for cancer researchers. Cancer Research . 2020;canres.0668.2020. doi: 10.1158/0008-5472.CAN-20-0668.

  • Yang X, Amgad M, ..., Ivanov AA. High expression of MKK3 is associated with worse clinical outcomes in African American breast cancer patients. J Transl Med. 2020;18(334). doi: 10.1186/s12967-020-02502-w

  • Amgad M, …, Salgado R, Cooper LAD. Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group. Npj Breast Cancer. 2020;6(16). doi: 10.1038/s41523-020-0154-2.

Report from the TIL Working Group, an international coalition of pathologists, oncologists, and statisticians.
  • Kos Z, …, Amgad M, …, Salgado R. Pitfalls in assessing stromal Tumour Infiltrating Lymphocytes (sTILs) in breast cancer. Npj Breast Cancer. 2020;6(17). doi: 10.1038/s41523-020-0156-0

  • Hudeček J et al. Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials. Npj Breast Cancer. 2020;6(15). doi: 10.1038/s41523-020-0155-1


  • Lee S, Amgad M, ..., Cooper L. An Ensemble-based Active Learning for Breast Cancer Classification. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, pp. 2549-2553. 2019. doi: 10.1109/BIBM47256.2019.8983317

  • Chandradevan R, ..., Amgad M, ..., Cooper LAD. Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells. Laboratory Investigations. 2019. doi: 10.1038/s41374-019-0325-7.

  • Amgad M, Elfandy H, …, Gutman DA, Cooper LAD. Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics. 2019. doi: 10.1093/bioinformatics/btz083

Lead a team of 25, including 2 pathologists and 3 pathology residents. Press coverage.Released one of largest semantic segmentation datasets in histology.
  • Amgad M, Sarkar A, …, Cooper LAD, Barnes M. Joint region and nucleus segmentation for characterization of Tumor Infiltrating Lymphocytes in breast cancer. Medical Imaging 2019: Digital Pathology, vol. 10956, p. 109560M. International Society for Optics and Photonics, 2019. doi: 10.1117/12.2512892


  • Elsebaie M, Amgad M, …, Elsayed Z. Management of low and intermediate risk adult rhabdomyosarcoma: A pooled survival analysis of 553 patients. Scientific Reports. 2018 Jun 19;8(1):9337. doi: 10.1038/s41598-018-27556-1.

Poster version won top spot at 2018 ACP Competition, Clinical Research Category.
  • Mobadersany P, Yousefi S, Amgad M, …, Cooper LAD. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc. of the Nat. Acad. of Science USA. 2018;115(13):E2970-E2979. doi: 10.1073/pnas.1717139115.

Featured on PNAS journal cover. Also featured by at least 7 news outlets.


  • Nalisnik M, Amgad M, …, Gutman DA, Cooper LAD. Interactive phenotyping of large-scale histology imaging data with HistomicsML. Scientific Reports. 2017;7(1):14588. doi: 10.1038/s41598-017-15092-3.

  • Yousefi S, Amrollahi F, Amgad M, …, Cooper LAD. Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models. Scientific Reports. 2017 Sep 15;7(1):11707. doi: 10.1038/s41598-017-11817-6.

Featured as 3rd top article in Oncology published in Nature Scientific Reports.


  • Tsui MM, Itoh A, Amgad M, et al. Vectors for genetically-encoded tags for Electron Microscopy contrast in Drosophila. Biol Proced Online. 2016 Feb 1;18:5. doi: 10.1186/s12575-016-0034-1.

  • Abdelfattah NS, Amgad M, et al. Molecular underpinnings of corneal angiogenesis: advances over the past decade. International Journal of Ophthalmology. 2016 May; 9(1). doi: 10.18240/ijo.2016.05.24.

  • Abdelfattah NS, Amgad M, et al. Host immune cellular reactions in corneal neovascularization: Review. International Journal of Ophthalmology. 2016 Apr; 9(1). doi: 10.18240/ijo.2016.04.25.


  • Amgad M, Itoh A, Tsui MM. Extending Ripley’s K-function to quantify aggregation in 2-D grayscale images. PLoS One. 2015;10(12):e0144404. doi: 10.1371/journal.pone.0144404.

  • Amgad M, Tsui MMK, …, Shash E. Medical Student Research: An Integrated Mixed-Methods Systematic Review and Meta-Analysis. PLoS One. 2015; 10(6): e0127470. doi: 10.1371/journal.pone.0127470.

  • Abdelfattah NS, Amgad M, et al. Clinical correlates of common corneal neovascular diseases: a literature review. International Journal of Ophthalmology. 2015; 8(1):182-93. doi: 10.3980/j.issn.2222-3959.2015.01.32.

  • Amgad M, Shash E. The evolution of undergraduate medical student research activities: Personal experience of a developing nation’s up-rise. J Cancer Education. 2015 Dec;30(4):813-4. doi: 10.1007/s13187-015-0923-z.


  • Amgad M, AlFaar AS. Integrating web 2.0 in clinical research education in a developing country. J Cancer Education. 2014 Sep;29(3):536-40. doi: 10.1007/s13187-013-0595-5.

  • Nageeb A, Amgad M,…, Emara K. Is the ISKD a safe measure for bone lengthening? A systematic literature review. J Orthopedic Trauma and Rehabilitation. 2014 Dec;18(2):69-78. doi: 10.1016/j.jotr.2014.01.002.

  • Abdelfattah NS, Amgad M, et al. Development of an Arabic version of the National Eye Institute Visual Function Questionnaire as a tool to study eye diseases patients in Egypt. International Journal of Ophthalmology. 2014 Oct 18;7(5):891-7. doi: 10.3980/j.issn.2222-3959.2014.05.27.


  • Amgad M, Shash E, Gaafar R. Cancer education for medical students in developing countries – Where do we stand and how to improve? Critical Reviews in Oncology Hematology. 2012 Oct;84(1):122-9. doi: 10.1016/j.critrevonc.2012.01.003.

Other publications


Amgad M, Salgado R, Cooper LAD. MuTILs: explainable, multiresolution computational scoring of Tumor-Infiltrating Lymphocytes in breast carcinomas using clinical guidelines. medRxiv 2022.01.08.22268814. 2021 Jan.

Zhang L, Amgad M, Cooper LAD. A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning. arXiv:2111.05882. 2021 Nov 10.

Yousefi S, Shaban A, Amgad M, Cooper LAD. Learning Cancer Outcomes from Heterogeneous Genomic Data Sources: An Adversarial Multi-task Learning Approach. ICML 2019 Workshop AMTL (Open Review). 2019.

Yousefi S, Shaban A, Amgad M, …, Cooper LAD. Learning Clinical Outcomes from Heterogeneous Genomic Data Sources. arXiv:1904.01637 [q-bio.QM]. 2019.

Amgad M, Fouad YA, Elsebaie MA. Approach to a highly-virulent emerging viral epidemic: A thought experiment and literature review. PeerJ Preprints. 2019. 7:e27518v1. doi: 10.7287/peerj.preprints.27518v1.


Tageldin MA, Cooper LAD, Kurkure U, Martin J. Image processing-based object classification bootstrapping of region-level annotations. Application US20220262145. Filing date: 10/31/2019. [PDF].

Sarkar A, Tageldin MA, Srinivas C. Deep-learning systems and methods for joint cell and region classification in biological images. Application WO-2019110583-A1. Legal status: Granted. 06/13/2019. [Google patents] [PDF].

Book chapters

Shash E, Amgad M. Global Perspectives on Cancer: Incidence, Care, and Experience. (Vol. 2, Ch. 20: Egypt). Feb 2015. Publisher: ABC-CLIO (Praeger). ISBN: 978-1-4408-2857-7. Book can be found here.

Conference abstracts and presentations

Oral presentations

Pathology Informatics Summit 2021 (Virtual):

Gallas BD*, Elfer K, Amgad M, ... . High Throughput Truthing (HTT): pathologist agreement from a pilot study.

Chicago Biomedical Informatics Data Jam 2020 (Chicago, IL)

Amgad M*. High-resolution mapping of the tumor microenvironment in breast carcinomas.

ACLPS 2020 (Virtual)

Drumheller B*, Amgad M, ..., Jaye D. Early Development of a Machine Learning Approach to Quantify MYC Immunohistochemical Staining in Lymphoma. American Journal of Clinical Pathology. 154(Supplement_1):S19-. 2020.

Path. Informatics 2019 (Pitt., PA)

Gallas BD*, Amgad M, et al. A Collaborative Project to Produce Regulatory-Grade Pathologist Annotations to Validate Viewers and Algorithms.

SPIE 2019 (San Diego, CA)

Amgad M*, Sarkar A, …, Cooper LAD, Barnes M. Joint region and nucleus segmentation for characterization of Tumor Infiltrating Lymphocytes in breast cancer. Medical Imaging 2019: Digital Pathology, vol. 10956, p. 109560M. 2019.

Dom. Viol. 2012 (Cairo, Egypt)

Magdy T, Amgad M*, Salama M et al. Prevalence and patterns of female genital mutilation and domestic violence among Cairo University medical students. Cairo University.

* Presenter

Poster presentations

Pathology Visions 2021 (Las Vegas, NV) || TRAVEL AWARD ||

Amgad M, Cooper LAD. Beyond saliency heatmaps: explaining deep-learning nucleus classification with intuitive decision tree approximations.

Pathology Informatics Summit 2021 (Virtual) || BEST POSTER AWARD || TRAINEE AWARD ||

Amgad M, Cooper LAD. MuTILs: a multiresolution approach for computational TILs assessment using clinical guidelines.

AACR 2020 (San Francisco, CA)

Saad AM, Elsebaie MAT, Amgad M, ..., Abdel-Rahman O. Racial disparities in pancreatic adenocarcinoma survival. Do they exist for patients who already survived their first year? [abstract]. AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2): Abstract B110.

USCAP 2020 (Los Angeles, CA)

Farris A, Vizcarra J, Amgad M, ..., Hogan J. Renal Allograft Image Analysis Pipeline Correlations with Interstitial Fibrosis Assessment. Laboratory Investigation. 2020;100(Suppl.1), 1574-1575.

ICML 2019 (Long Beach, CA)

Yousefi S, Shaban A, Amgad M, Cooper LAD. Learning cancer outcomes from heterogeneous genomic data sources: An adversarial multi-task learning approach.

AACR 2019 (Atlanta, GA)

Amgad M, Kurkure U, …, Cooper LAD. Systematic computational analysis of histologic-genomic associations in triple-negative infiltrating ductal carcinomas of the breast [abstract]. Cancer Research. 2019;79(13)-suppl.

SABCS 2018 (San Antonio, TX)

Amgad M, Sarkar A, …, Cooper LAD, Barnes M. Computational scoring of tumor infiltrating lymphocytes in triple-negative breast cancer [abstract]. Cancer Research. 2019;79(4)-suppl.

Path. Visions 2018 (San Diego, CA)

Amgad M, Elfandy H, ... , Cooper LAD. Hierarchical Crowdsourcing for Generating Large-Scale Annotations of Histopathology. J Pathology Informatics. 2019;10:10.

ACLPS 2018 (Houston, TX)

Abdulrahman A, Amgad M, Cooper LAD, Jaye D. Early Experience in Developing a Machine-Learning and Digital Pathology Approach to Automate Bone Marrow Differential Counts. American J. of Clinical Pathology. 2018;150 (suppl_1),S149-S150.

ACP 2018 (New Orleans, LA) || BEST POSTER AWARD ||

Elsebaie M, Amgad M, …, Elsayed Z. Management of low and intermediate-risk adult rhabdomyosarcoma: A pooled survival analysis of 553 patients. Sci. Rep. 2018;8(1):9337.

AANS/CNS 2017 (Houston, TX)

Halani SH, et al. Markers and Mechanisms of Disease Progression in IDH-Mutant Astrocytomas. Neurosurgery. 2017; 64(CN_suppl_1):261–262.

ARVO 2013 (Washnigton, DC)

Saleh N et al. Development of an Arabic Version of the National Eye Institute Visual Function Questionnaire as a tool to study eye diseases patients in Egypt. Invest Ophthalmol Vis Sci 2013;54: E-Abstract 5335.

ESOT 2013 (Vienna, AU)

Elkhashab SA et al. Quality of life in dialysis patients: Impact of a functioning graft. Transplant International 26 (Suppl. 2), 185–339, P021.