Quantitative Imaging and AI Laboratory The William States Lee College of Engineering
Quantitative Imaging and AI Laboratory

Publications

Full publications list: https://scholar.google.com/citations?user=Z8pB1tQAAAAJ&hl=en

Published Journals

  1. Haghighi, T., Gholami, S., Sokol, J. T., Kishnani, E., Ahsaniyan, A., Rahmanian, H., Hedayati, F., Leng, T., & Alam, M. N. (2024). EYE-Llama, an in-domain large language model for ophthalmology. bioRxiv : the preprint server for biology, 2024.04.26.591355. https://doi.org/10.1101/2024.04.26.591355
  2. Badhon RH, Thompson AC, Lim JI, Leng T, Alam MN. Quantitative Characterization of Retinal Features in Translated OCTA. arXiv. 2024;2404.16133.
  3. Jannat FE, Gholami S, Alam MN, Tabkhi H. OCT-SelfNet: A Self-Supervised Framework with Multi-Modal Datasets for Generalized and Robust Retinal Disease Detection. arXiv. 2024;2401.12344.
  4. Badhon RH, Thompson AC, Lim JI, Leng T, Alam MN. Quantitative Characterization of Retinal Features in Translated OCTA. medRxiv. 2024;2024.02.23.24303275. doi:10.1101/2024.02.23.24303275.
  5. Gholami S, Leng T, Alam MN. A federated learning framework for training multi-class OCT classification models. Invest Ophthalmol Vis Sci. 2023;64(9):PB005-PB005.
  6. Alam, M.N., Yamashita, R., Ramesh, V. et al. Contrastive learning-based pretraining improves representation and transferability of diabetic retinopathy classification models. Sci Rep 13, 6047 (2023). https://doi.org/10.1038/s41598-023-33365-y.
  7. Alam M, Zhao EJ, Lam CK, Rubin DL. Segmentation-Assisted Fully Convolutional Neural Network Enhances Deep Learning Performance to Identify Proliferative Diabetic Retinopathy. Journal of Clinical Medicine. 2023; 12(1):385. https://doi.org/10.3390/jcm12010385.
  8. Rikiya Yamashita*, Tara Kapoor*, Minhaj Alam (equal contribution)*, et. al., “Toward Reduction in False-Positive Thyroid Nodule Biopsies with a Deep Learning-based Automated Risk Stratification System using Ultrasound Cine-clip Images,” Radiology AI (2022).
  9. Minhaj Alam, Joelle Hallak, “AI-automated referral for patients with visual impairment,” Lancet Digital Health (2021), 3 (1), e2-e3, (Invited commentary) (2022).
  10. Kate Romond, Minhaj Alam, Sasha Kravets, Luis de Sisternes, Theodore Leng, Jennifer I. Lim, Daniel L. Rubin, Joelle A. Hallak, “Imaging and artificial intelligence for progression of age-related macular degeneration: A mini review” (Invite review paper – Experimental biology and medicine (2022).
  11. Minhaj Alam, David Le, and Xincheng Yao, “Differential artery-vein analysis in quantitative retinal imaging: a review” Quantitative Imaging in Medicine and Surgery (2021), 11 (3), 1102.
  12. Minhaj Alam, David Le, Taeyoon Son, Jennifer I. Lim, and Xincheng Yao, “AV-Net: deep learning for fully automated artery-vein classification in optical coherence tomography angiography,” Biomed. Opt. Express 11, 5249-5257 (2020).
  13. Minhaj Alam, David Le, Jennifer I. Lim, Xincheng Yao, “Vascular Complexity Analysis in Optical Coherence Tomography Angiography of Diabetic Retinopathy,” Retina (Philadelphia, PA), (2020)
  14. David Le, Minhaj Alam, Cham Yao, Jennifer I. Lim, Yi-Ting Hsieh, RVP Chan, Devrim Toslak, and Xincheng Yao, Transfer learning for automated OCTA detection of diabetic retinopathy. Trans. Vis. Sci. Tech. (TVST), 9(2), pp.35-35.
  15. Xincheng Yao, Minhaj Alam, David Le, Devrim Toslak, “Quantitative Optical Coherence Tomography Angiography Analysis and Classification: A review,” Experimental biology and medicine (2020), 245 (4), 301-302.
  16. Minhaj Alam, David Le, Jennifer I. Lim, Devrim Toslak, Xincheng Yao, “Supervised machine learning based multi-task artificial intelligence classification of retinopathies,” Journal of Clinical Medicine (2019), 8 (6), 872.
  17. Yi-Ting Hsieh, Minhaj Alam, Daniel Chao, Xincheng Yao, “Optical Coherence Tomography Angiography Biomarkers for Predicting Visual Outcomes after Ranibizumab Treatment for Diabetic Macular Edema,” Ophthalmology Retina 3 (10), 826-834. (2019).
  18. David Le, Minhaj Alam, Jennifer. I. Lim, Xincheng Yao, “Quantitative geometric features in optical coherence tomography angiography of diabetic retinopathy,” Biomed. Opt. Express, 10 (5), 2493-2503 (2019). (Co-first author).
  19. Taeyoon Son, Minhaj Alam, Changgeng Liu , Devrim Toslak , Xincheng Yao, “Optical coherence tomography guided artery-vein classification in retinal OCT angiography of macular region,” Experimental biology and medicine (2019), p.1535370219850791.
  20. Minhaj Alam, Devrim Toslak, Jennifer I. Lim, Xincheng Yao, “OCT feature analysis guided artery-vein differentiation in OCTA,” Biomed. Opt. Express, 10 (4), 2055-2066(2019).
  21. Minhaj Alam, Jennifer I. Lim, Devrim Toslak, Xincheng Yao, “Differential artery-vein analysis improves the performance of OCTA staging of sickle cell retinopathy,” Trans. Vis. Sci. Tech. (TVST) 8 (2), 3-3 (2019).
  22. Minhaj Alam, Yue Zhang, Jennifer I. Lim, R.V.P. Chan, Min Yang, Xincheng Yao, “Quantitative OCT angiography features for objective classification and staging of diabetic retinopathy,” Retina (Philadelphia, PA), (2018) DOI: 10.1097/IAE.0000000000002373.
  23. Minhaj Alam, Devrim Toslak, Jennifer I. Lim, Xincheng Yao, “Color fundus image guided artery-vein differentiation in optical coherence tomography angiography,” Investigative Ophthalmology & Visual Science (IOVS), Vol.59, 4953-4962 (2018).
  24. Minhaj Alam, Taeyoon Son, Devrim Toslak, Jennifer I. Lim, Xincheng Yao, “Combining ODR and Blood Vessel Tracking for Artery–Vein Classification and Analysis in Color Fundus Images,” Trans. Vis. Sci. Tech. (TVST), 7(2):23 (2018).
  25. Taeyoon Son, Minhaj Alam, Devrim Toslak, Benquan Wang, Yiming Lu, Xincheng Yao, “Functional optical coherence tomography of neurovascular coupling interactions in the retina,” Journal of Biophotonics, e201800089 (2018).
  26. Devrim Toslak, Changgeng Liu, Minhaj Alam, Xincheng Yao, “Near infrared light guided miniaturized indirect ophthalmoscopy for nonmydriatic wide-field fundus photography,” Optics Letters, 43 (11) p 2551-4. (2018).
  27. Benquan Wang, Devrim Toslak, Minhaj Alam., R.V.P. Chan, Xincheng Yao, “Contact-free trans-pars-planar illumination enables snapshot fundus camera for nonmydriatic wide field photography,” Scientific reports, 8(1) p 8768 (2018).
  28. Tae-Hoon Kim, Taeyoon Som, Yiming Lu, Minhaj Alam, Xincheng Yao, “Comparative optical coherence tomography angiography of wild-type and rd10 mouse retinas,” Trans. Vis. Sci. Tech. (TVST), 7(6):42 (2018).
  29. Minhaj Alam, Damber Thapa, Jennifer I. Lim, Dingcai Cao, and Xincheng Yao, “Computer-aided classification of sickle cell retinopathy using quantitative features in optical coherence tomography angiography,” Biomed. Opt. Express 8, 4206-4216 (2017).
  30. Minhaj Alam, Damber Thapa, Jennifer I. Lim, Dingcai Cao, and Xincheng Yao, “Quantitative characteristics of sickle cell retinopathy in optical coherence tomography angiography,” Biomed. Opt. Express 8, 1741-1753 (2017).
  31. Changgeng Liu, et al. “In vivo super-resolution retinal imaging through virtually structured detection,” Journal of Biomedical Optics 21.12 (2016): 120502-120502.

Journals in production/preparation

  1. Minhaj Alam, Emma Zhao, Carson Lam, Joelle Hallak, and Daniel Rubin, “Segmentation assisted fully convolutional neural network enhances deep learning performance to identify proliferative diabetic retinopathy”, RETINA (2021) (Under Revision).
  2. Minhaj Alam, Josiah Aklilu, Joelle Hallak, Nooshin Moojab, and Daniel Rubin, “Doing more with less – A contrastive learning pipeline for robust diabetic retinopathy classification,” Lancet Digital Health (2021) (In Preparation).
  3. Minhaj Alam, Kapil Mishra, Ted Leng, Joelle Hallak, and Daniel Rubin, “Cross-Modal Data Programming for multi-class classification of diabetic retinopathy,” Nature Digital Medicine (2021) (In preparation).
  4. Minhaj Alam, Joelle Hallak, Maximilian Pfau, and Daniel Rubin, “An automated segmentation pipeline for hyper-reflective foci with pre-processed region of interest selection,” JAMA Ophthalmology (2021) (In preparation).

Conference Proceedings

  1. David Le, Minhaj Alam, Xincheng Yao, “Deep learning artery-vein classification in OCT Angiography”, In Ophthalmic Technologies XXVIII, SPIE Photonics West 2021.
  2. Minhaj Alam, David Le, Jennifer I. Lim, Xincheng Yao, “Quantitative analysis of vascular complexity in OCTA of diabetic retinopathy,” In Ophthalmic Technologies XXVIII, SPIE Photonics West 2020.
  3. Minhaj Alam, Taeyoon Son, Devrim Toslak, Jennifer I. Lim, and Xincheng Yao, “Quantitative artery-vein analysis in optical coherence tomography angiography,” In Ophthalmic Technologies XXVIII, SPIE Photonics West 2019, vol. 10858, p. 1085802. International Society for Optics and Photonics, 2019.
  4. Taeyoon Son, Minhaj Alam, Changgeng Liu, Devrim Toslak, and Xincheng Yao, “Artery and vein differentiation in retinal optical coherence tomography angiography of macular region,” In Ophthalmic Technologies XXVIII, SPIE Photonics West 2019, vol. 10858, p. 108581L. International Society for Optics and Photonics, 2019.
  5. Minhaj Alam, Taeyoon Son, Devrim Toslak, Jennifer I. Lim, and Xincheng Yao. “Automated classification and quantitative analysis of arterial and venous vessels in fundus images.” In Ophthalmic Technologies XXVIII, SPIE Photonics West 2018, vol. 10474, p. 1047426. International Society for Optics and Photonics, 2018.
  6. Devrim Toslak, Benquan Wang, Minhaj Alam, and Xincheng Yao, “Nonmydriatic single-shot widefield fundus camera with trans-pars planar illumination (Conference Presentation).” In Ophthalmic Technologies XXVIII, SPIE Photonics West 2018, vol. 10474, p. 1047414. International Society for Optics and Photonics, 2018.
  7. Minhaj Alam, Mohammed Naser, “Re-evaluating Chain-Code as Features for Bangla Script”, International conference on Electrical Information and Communication Technology (EICT-2014), KUET, Khulna, Bangladesh. DOI:10.1109/EICT.2014.6777865. IEEE Xplore (2014).

Abstracts in conferences

  1. Minhaj Alam, Ted Leng, Joelle Hallak, and Daniel Rubin, “Contrastive learning improves representation and transferability of diabetic retinopathy classification models”(Oral Presentation in ARVO 2022, Hot topic, winner of Retina Research Foundation Travel Grant).
  2. Nooshin Mojab, Minhaj Alam, and Joelle Hallak, “FundusNet, A self-supervised contrastive learning framework for Fundus Feature Learning” (Poster at ARVO 2022).
  3. S Sahu, L De Sisternes, Minhaj Alam, et. al., “Optimal Selection of Longitudinally Measured Imaging Biomarkers Affecting Conversion Time to Neovascular AMD Using a Regularized Multivariate Bayesian Joint Model” (Poster at ARVO 2022).
  4. A Sethi, Minhaj Alam, et. al., “Methods for Manual Segmentation of Hyper-resonant Foci to Identify a Ground Truth for Deep Learning Models” (Poster at ARVO 2022). 
  5. Minhaj Alam, et. al., “Automated region of interest selection improves the deep learning based segmentation of hyper-reflective foci in optical coherence tomography images.” (Poster in ARVO 2021, Travel Grant)
  6. Taylor Shagam, Minhaj Alam, et. al., “Deep learning based binary image quality algorithm for low-cost fundus imaging system in remote care settings” (Poster in ARVO 2021).
  7. Hugang Ren, Simon Bello, Minhaj Alam, et. al., “Optimizing inference performance of a fundus image quality neural network model for edge computing using TensorFlow Lite” (Poster in ARVO 2021).
  8. Minhaj Alam, et. al., “Deep learning for artery-vein classification in OCT angiography” (Poster in ARVO 2020).
  9. David Le, Minhaj Alam, et. al., “Deep Machine Learning for OCTA Classification of Diabetic Retinopathy” (Poster in ARVO 2020).
  10. Minhaj Alam, et.al., “Supervised machine learning based multi-task artificial intelligence classification of retinopathies.” (Oral presentation in IOCS 2019 Annual Meeting)
  11. Minhaj Alam, et. al., “En-face OCT feature analysis enables objective artery-vein differentiation in OCT angiography.” (Oral presentation (HOT Topic) in ARVO 2019).
  12. David Le, Minhaj Alam, Jennifer I. Lim, Xincheng Yao, “Quantitative geometric features in optical coherence tomography angiography of diabetic retinopathy.” (Poster presentation in ARVO 2019).
  13. Minhaj Alam, et. al. “Quantitative OCT angiography for computer aided classification of diabetic retinopathy.” (2018) (Oral presentation (HOT topic) in ARVO 2018).
  14. Taeyoon Son, Minhaj Alam, et. al., “OCTA-guided functional OCT imaging of retinal neural activation and hemodynamic response.” (Oral presentation in ARVO 2018).
  15. Alejandra Main, Minhaj Alam, et. al., “Quantitative Analysis of OCT Angiography findings in Birdshot Chorioretinopathy.” (Poster in ARVO 2018).
  16. Devrim Toslak, Changgeng liu, Minhaj Alam, et. al., “Miniaturized indirect ophthalmoscopy for nonmydriatic wide-field fundus photography.” (Poster in ARVO 2018).
  17. Sarwar Zahid, Minhaj Alam, et. al., “Quantitative Optical Coherence Tomography Angiography Parameters in Central Retinal Vein Occlusion.” (Poster in ARVO 2018).
  18. Judy Chen, Sarwar Zahid, Minhaj Alam, et. al., “Assessment of Quantitative Optical Coherence Tomography Angiography Parameters in Branch Retinal Vein Occlusion and Monitoring Response to Treatment.” (Poster in ARVO 2018).
  19. Jennifer I. Lim, Minhaj Alam, et. al., “Quantitative analysis of OCT Angiography novel parameters in a comparison of sickle cell retinopathy to control eyes.” (Oral presentation in Macula Society Meeting 2018).
  20. Jennifer I. Lim, Minhaj Alam, et. al., “Quantitative Comparative Analysis of OCTA Images Using Novel Parameters: Comparison of Control and Sickle Cell Retinopathy Eyes.” (Podium presentation, Retina Society Meeting 2018).
  21. Yi-Ting Hsieh, Minhaj Alam, et. al., “Correlations of baseline optical coherence tomography angiography biomarkers with visual improvement after ranibizumab treatment for diabetic macular edema.” (Poster presentation in APVRS Annual Congress, 2018).
  22. Minhaj Alam, et al. “Automatic classification of sickle cell retinopathy using quantitative features in optical coherence tomography angiography.” (Poster presentation in ARVO 2017).
  23. Minhaj Alam, et al. “Effect of noise reduction in virtually structured detection based super-resolution Scanning Laser Microscopy.” (Poster presentation in ARVO 2016).
  24. Jennifer I. Lim, Minhaj Alam, et. al., “Quantitative OCT Angiography analysis for classification and staging of non-proliferative diabetic retinopathy.” (Oral presentation in Macula Society Meeting 2019).

Book Chapters

  • David Le, Taeyoon Son, Jennifer Lim, and Xincheng Yao, “Quantitative features for objective assessment of OCT angiography” Photo Acoustic and Optical Coherence Tomography Imaging 3, 7-1, 2021

Invited talks

  1. “Self supervision in Ophthalmic Diagnostics”. (Stanford MedAI seminar series, 2021).
  2. “A contrastive learning framework for classification of diabetic retinopathy”. (Stanford IBIIS and AIMI seminar series 2020, 2021).
  3. “Quantitative analysis of ophthalmic imaging biomarkers”. (Dept. Biomedical Engineering, University of Arkansas, 2021).
  4. , “Quantitative analysis and automated classification of retinal diseases in optical coherence tomography angiography.” (Dept. of Ophthalmology, Northwestern University, 2020, 2017).
  5. , “Super-resolution microscopy through virtually structured detection for low signal levels.” (BioE 102, Dept. of Bioengineering, UIC, 2016).

Patents

  1. Xincheng Yao, Minhaj Alam, and Taeyoon Son “Supervised machine learning based multi-class artificial intelligence classification of retinopathies”, US Patent 17/438,600
  2. Minhaj Alam, “OCT feature analysis guided artery-vein differentiation in OCTA”, (Provisional patent submitted in the USPTO on March 13, 2019)