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Faisal Mahmood, Ph.D.

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Overview
Mahmood Lab aims to utilize machine learning, data fusion, and medical image analysis to develop streamlined workflows for cancer diagnosis, prognosis, and biomarker discovery. We are interested in developing automated and objective mechanisms for reducing interobserver and intraobserver variability in cancer diagnosis using artificial intelligence as an assistive tool for pathologists. The lab also focuses on the development of new algorithms and methods to identify clinically relevant morphologic phenotypes and biomarkers associated with response to specific therapeutic agents. We develop multimodal fusion algorithms for combining information from multiple imaging modalities, familial and patient histories and multi-omics data to make more precise diagnostic, prognostic and therapeutic determinations.

Bibliographic
Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Faculty can login to make corrections and additions.
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PMC Citations indicate the number of times the publication was cited by articles in PubMed Central, and the Altmetric score represents citations in news articles and social media. (Note that publications are often cited in additional ways that are not shown here.) Fields are based on how the National Library of Medicine (NLM) classifies the publication's journal and might not represent the specific topic of the publication. Translation tags are based on the publication type and the MeSH terms NLM assigns to the publication. Some publications (especially newer ones and publications not in PubMed) might not yet be assigned Field or Translation tags.) Click a Field or Translation tag to filter the publications.
  1. Mahmood F, Zhang H, Lin J, Wan C. Laser-Induced Graphene Derived from Kraft Lignin for Flexible Supercapacitors. ACS Omega. 2020 Jun 23; 5(24):14611-14618. PMID: 32596598.
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  2. Chen MT, Mahmood F, Sweer JA, Durr NJ. GANPOP: Generative Adversarial Network Prediction of Optical Properties From Single Snapshot Wide-Field Images. IEEE Trans Med Imaging. 2020 Jun; 39(6):1988-1999. PMID: 31899416.
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  3. Mahmood F, Borders D, Chen R, McKay GN, Salimian KJ, Baras A, Durr NJ. Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images. IEEE Trans Med Imaging. 2019 Jul 05; PP. PMID: 31283474.
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  4. Bobrow TL, Mahmood F, Inserni M, Durr NJ. DeepLSR: a deep learning approach for laser speckle reduction. Biomed Opt Express. 2019 Jun 01; 10(6):2869-2882. PMID: 31259057.
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  5. Mahmood, F., Borders, D., Chen, R., McKay, G.N., Salimian, K.J., Baras, A. and Durr, N.J., . Deep adversarial training for multi-organ nuclei segmentation in histopathology images. IEEE Transactions on Medical Imaging. 2019. View Publication.
  6. Bobrow, T.L., Mahmood, F., Inserni, M. and Durr, N.J.,. DeepLSR: Deep learning approach for laser speckle reduction. Biomedical Optics Express. 2019. View Publication.
  7. McKay GN, Mahmood F, Durr NJ. Large dynamic range autorefraction with a low-cost diffuser wavefront sensor. Biomed Opt Express. 2019 Apr 01; 10(4):1718-1735. PMID: 31061764.
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  8. McKay, G.N., Mahmood, F. and Durr, N.J. Large dynamic range autorefraction with a low-cost diffuser wavefront sensor. Biomedical Optics Express. 2019; 10(4):1718-1735. View Publication.
  9. Mahmood, F., Toots, M., Öfverstedt, L.G.W. and Skoglund, B.U. 2D discrete Fourier transform with simultaneous edge artifact removal for real-time applications US Patent US20180204313A1. 2018. View Publication.
  10. Mahmood F, Chen R, Sudarsky S, Yu D, Durr NJ. Deep learning with cinematic rendering: fine-tuning deep neural networks using photorealistic medical images. Phys Med Biol. 2018 09 13; 63(18):185012. PMID: 30113015.
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  11. Mahmood F, Durr NJ. Deep learning and conditional random fields-based depth estimation and topographical reconstruction from conventional endoscopy. Med Image Anal. 2018 08; 48:230-243. PMID: 29990688.
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  12. Mahmood F, Chen R, Durr NJ. Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training. IEEE Trans Med Imaging. 2018 12; 37(12):2572-2581. PMID: 29993538.
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  13. Mahmood, F., Shahid, N., Skoglund, U. and Vandergheynst, P. Adaptive Graph-Based Total Variation for Tomographic Reconstructions. IEEE Signal Processing Letters. 2018; 25(5):700-704. View Publication.
  14. Mahmood, F., Öfverstedt, L.G., Toots, M., Wilken, G. and Skoglund, U. An Extended Field-Based Method for Noise Removal From Electron Tomographic Reconstructions. IEEE Access. 2018; 6:17326-17339. View Publication.
  15. Mahmood, F. and Durr, N.J., . Topographical reconstructions from monocular optical colonoscopy images via deep learning. 15th IEEE International Symposium on Biomedical Imaging. 2018; 216-219. View Publication.
  16. Mahmood, F., Toots, M., Öfverstedt, L.G. and Skoglund, U. Algorithm and Architecture Optimization for 2D Discrete Fourier Transforms with Simultaneous Edge Artifact Removal. International Journal of Reconfigurable Computing. 2017.
  17. Mahmood F, Johannesen HH, Geertsen P, Hansen RH. Repeated diffusion MRI reveals earliest time point for stratification of radiotherapy response in brain metastases. Phys Med Biol. 2017 04 21; 62(8):2990-3002. PMID: 28306548.
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  18. Mahmood, F., Öfverstedt, L.G.W. and Skoglund, B.U.,. Extended field iterative reconstruction technique (EFIRT) for correlated noise removal. US Patent US9594032B2. 2017. View Publication.
  19. Mahmood F. Algorithmic and Architectural Developments for Cryo-Electron Tomography. 2017. View Publication.
  20. Mahmood F, Shahid N, Vandergheynst P, Skoglund U. Graph-based sinogram denoising for tomographic reconstructions. Conf Proc IEEE Eng Med Biol Soc. 2016 08; 2016:3961-3664. PMID: 28269152.
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  21. Mohan DM, Kumar P, Mahmood F, Wong KF, Agrawal A, Elgendi M, Shukla R, Ang N, Ching A, Dauwels J, Chan AH. Effect of Subliminal Lexical Priming on the Subjective Perception of Images: A Machine Learning Approach. PLoS One. 2016; 11(2):e0148332. PMID: 26866807.
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  22. Mahmood, F., Toots, M., Öfverstedt, L.G. and Skoglund, U., . 2D discrete Fourier transform with simultaneous edge artifact removal for real-time applications. IEEE International Conference on Field Programmable Technology (FPT). 2015; 236-239. View Publication.
  23. Kumar P, Mahmood F, Mohan DM, Wong K, Agrawal A, Elgendi M, Shukla R, Dauwels J, Chan AH. On the effect of subliminal priming on subjective perception of images: a machine learning approach. Conf Proc IEEE Eng Med Biol Soc. 2014; 2014:5438-41. PMID: 25571224.
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Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.