Biography

I am currently a Principal Data Scientist in the Artificial Intelligence & Computational Sciences team in Biomedical Research at Novartis. Prior to this, I was a Data Scientist in the Digital Pathology Machine Learning team at Verily Life Sciences. I completed my PhD in Biomedical Engineering at Duke University and BEng/MEng in Biomedical Engineering at Imperial College London. My research focuses on machine learning for medical image analysis and clinical applications.

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Dissertation

Deep Learning Image Analysis Framework for Clinical Management of Retinal and Corneal Diseases
J. Loo
Duke University, 2022
Recipient of the Biomedical Engineering Doctoral Dissertation Award

Journal Publications / Preprints

2024

Autofluorescence Virtual Staining System for H&E Histology and Multiplex Immunofluorescence Applied to Immuno-Oncology Biomarkers in Lung Cancer
J. Loo*, M. Robbins*, C. McNeil, T. Yoshitake, C. Santori, C. J. Shan, S. Vyawahare, H. Patel, T. C. Wang, R. Findlater, D. F. Steiner, S. Rao, M. Gutierrez, Y. Wang, A. C. Sanchez, R. Yin, V. Velez, J. S. Sigman, P. Coutinho de Souza, H. Chandrupatla, L. Scott, S. S. Weaver, C. W. Lee, E. Rivlin, R. Goldenberg, S. S. Couto, P. Cimermancic, and P. F. Wong
Preprint (Under Review), medRxiv 2024.06.12.24308841, June 2024

2023

Joint Multimodal Deep Learning-Based Automatic Segmentation of ICGA and OCT Images for Assessment of PCV Biomarkers
J. Loo, K. Y. C. Teo, C. H. Vyas, J. M. N. Jordan-Yu, A. B. Juhari, G. J. Jaffe, C. M. G. Cheung, and S. Farsiu
Ophthalmology Science 3(3), 100292, September 2023

Domain-Specific Optimization and Diverse Evaluation of Self-Supervised Models for Histopathology
J. Lai, F. Ahmed, S. Vijay, T. Jaroensri, J. Loo, S. Vyawahare, S. Agarwal, F. Jamil, Y. Matias, G. S. Corrado, D. R. Webster, J. Krause, Y. Liu, P. H. C. Chen, E. Wulczyn, and D. F. Steiner
Preprint, arXiv:2310.13259, October 2023
Related Blog Post: Health-Specific Embedding Tools for Dermatology and Pathology

2022

Validation of a Deep Learning-Based Algorithm for Segmentation of the Ellipsoid Zone on Optical Coherence Tomography Images of an USH2A-Related Retinal Degeneration Clinical Trial
J. Loo, G. J. Jaffe, J. L. Duncan, D. G. Birch, and S. Farsiu
Retina 42(7), 1347-1355, July 2022

Deep Learning-Based Classification and Segmentation of Retinal Cavitations on Optical Coherence Tomography Images of Macular Telangiectasia Type 2
J. Loo, C. X. Cai, J. Choong, E. Y. Chew, M. Friedlander, G. J. Jaffe, and S. Farsiu
British Journal of Ophthalmology 106(3), 396-402, February 2022

Impact of Baseline Quantitative OCT Features on Response to Risuteganib for the Treatment of Dry AMD – The Importance of Outer Retinal Integrity
J. R. Abraham, G. J. Jaffe, P. K. Kaiser, S. J. Chiu, J. Loo, S. Farsiu, L. Bouckaert, V. Karageozian, M. Sarayba, S. K. Srivastava, and J. P. Ehlers
Ophthalmology Retina 6(11), 1019-1027, November 2022

Baseline Microperimetry and OCT in the RUSH2A Study: Structure-Function Association and Correlation with Disease Severity
E. M. Lad, J. L. Duncan, W. Liang, M. G. Maguire, A. R. Ayala, I. Audo, D. G. Birch, J. Carroll, J. K. Cheetham, T. A. Durham, A. T. Fahim, J. Loo, Z. Deng, D. Mukherjee, E. Heon, R. B. Hufnagel, B. Guan, A. Iannaccone, G. J. Jaffe, C. N. Kay, M. Michaelides, M. E. Pennesi, A. Vincent, C. Y. Weng, and S. Farsiu
American Journal of Ophthalmology 244, 98-116, December 2022

2021

Open-Source Automatic Biomarker Measurement on Slit-Lamp Photography to Estimate Visual Acuity in Microbial Keratitis
J. Loo, M. A. Woodward, V. Prajna, M. F. Kriegel, M. Pawar, M. Khan, L. M. Niziol, and S. Farsiu
Translational Vision Science & Technology 10(12), 2, October 2021

Open-Source Automatic Segmentation of Ocular Structures and Biomarkers of Microbial Keratitis on Slit-Lamp Photography Images Using Deep Learning
J. Loo, M. F. Kriegel, M. M. Tuohy, K. H. Kim, V. Prajna, M. A. Woodward, and S. Farsiu
IEEE Journal of Biomedical and Health Informatics 25(1), 88-99, January 2021

Comparison of Single Drusen Size on Color Fundus Photography and Spectral-Domain Optical Coherence Tomography
D. Y. Kim, J. Loo, S. Farsiu, and G. J. Jaffe
Retina 41(8), 1715-1722, August 2021

Intraoperative Retinal Changes May Predict Surgical Outcomes After Epiretinal Membrane Peeling
L. K. Mukkamala, J. Avaylon, R. J. Welch, A. Yazdanyar, P. Emami-Naeini, S. Wong, J. Storkersen, J. Loo, D. Cunefare, S. Farsiu, A. Moshiri, S. S. Park, and G. Yiu
Translational Vision Science & Technology 10(2), 36, February 2021

2020

Beyond Performance Metrics: Automatic Deep Learning Retinal OCT Analysis Reproduces Clinical Trial Outcome
J. Loo, T. E. Clemons, E. Y. Chew, M. Friedlander, G. J. Jaffe, and S. Farsiu
Ophthalmology 127(6), 793-801, June 2020
Related Commentary: The Machines Are Coming

Measurement Reliability for Keratitis Morphology
M. F. Kriegel, J. Loo, S. Farsiu, V. Prajna, M. Tuohy, K. H. Kim, A. N. Valicevic, L. M. Niziol, H. Tan, H. A. Ashfaq, D. Ballouz, and M. A. Woodward
Cornea 39(12), 1503-1509, December 2020

2019

Computational Modeling of Retinal Hypoxia and Photoreceptor Degeneration in Patients with Age-Related Macular Degeneration
K. J. McHugh, D. Li, J. C. Wang, L. Kwark, J. Loo, V. Macha, S. Farsiu, L. A. Kim, and M. Saint-Geniez
PLOS ONE 14(6), e0216215, June 2019

2018

Deep Longitudinal Transfer Learning-Based Automatic Segmentation of Photoreceptor Ellipsoid Zone Defects on Optical Coherence Tomography Images of Macular Telangiectasia Type 2
J. Loo, L. Fang, D. Cunefare, G. J. Jaffe, and S. Farsiu
Biomedical Optics Express 9(6), 2681-2698, June 2018

2016

Modeling the Biomechanics of Fetal Movements
S. W. Verbruggen, J. Loo, T. T. A. Hayat, J. V. Hajnal, M. A. Rutherford, A. T. M. Phillips, and N. C. Nowlan
Biomechanics and Modeling in Mechanobiology 15(4), 995-1004, August 2016

Conference Presentations / Abstracts

2024

Prediction of KRAS Mutation Status from H&E Foundation Model Embeddings in Non-Small Cell Lung Cancer
M. Robbins*, J. Loo*, S. Vyawahare, Y. Wang, C. McNeil, S. Rao, P. F. Wong, E. Rivlin, S. S. Weaver, and R. Goldenberg
MICCAI Workshop on Computational Pathology with Multimodal Data (COMPAYL), October 2024

Prediction of MASH Features from Liver Biopsy Images Using a Pretrained Self-Supervised Learning Model
Y. Wang, S. Vyawahare, C. McNeil, J. Loo, M. Robbins, and R. Goldenberg
EASL Congress, June 2024 (Poster)

Predicting Immunotherapy Outcomes from H&E Images in Lung Cancer
J. Loo, Y. Wang, P. F. Wong, E. Wulczyn, J. Lai, P. Cimermancic, D. F. Steiner, and S. S. Weaver
AACR Annual Meeting, April 2024 (Poster)

2021

The RUSH2A Study: Baseline Microperimetry and SD-OCT Measures
E. M. Lad, W. Liang, G. J. Jaffe, Z. Deng, J. Loo, D. Mukherjee, and S. Farsiu
ARVO Annual Meeting, May 2021 (Virtual)

The RUSH2A Study: Microperimetry and SD-OCT Measures at Baseline
E. M. Lad, W. Liang, G. J. Jaffe, Z. Deng, J. Loo, D. Mukherjee, and S. Farsiu
44th Annual Macula Society Meeting, February 2021 (Virtual)

Comparison of Single Drusen Size on Color Fundus Photography and Spectral Domain Optical Coherence Tomography
G. J. Jaffe, D. Y. Kim, J. Loo, and S. Farsiu
44th Annual Macula Society Meeting, February 2021 (Virtual)

2020

Deep Learning-Based Automatic Segmentation of Retinal Cavitations on OCT Images of MacTel2
J. Loo, C. X. Cai, E. Y. Chew, M. Friedlander, G. J. Jaffe, and S. Farsiu
ARVO Annual Meeting, May 2020 (Virtual)

Deep Learning-Based Automatic Segmentation of Intact Ellipsoid Zone Area on Optical Coherence Tomography Images of USH2A-Related Retinal Degeneration
S. Farsiu, J. Loo, J. L. Duncan, D. G. Birch, and G. J. Jaffe
ARVO Annual Meeting, May 2020 (Virtual)

In-Vivo Quantitative Analysis of Pterygium Volume Using Anterior Segment Optical Coherence Tomography Imaging
S. Onal, J. Loo, T. Nguyen, M. Cherukury, S. Farsiu, and G. J. Jaffe
ARVO Annual Meeting, May 2020 (Virtual)

Meta-Learning Approach to Automatically Register Multivendor Retinal Images
A. Hasan, Z. Deng, J. Loo, D. Mukherjee, J. L. Duncan, D. G Birch, G. J. Jaffe, and S. Farsiu
ARVO Annual Meeting, May 2020 (Virtual)

2019

Automatic Deep Learning OCT Analysis Algorithm Reliably Reproduces Expert-Evaluated Outcome of a Randomized Clinical Trial for Macular Telangiectasia Type 2 Treatment
J. Loo, T. E. Clemons, E. Y. Chew, M. Friedlander, G. J. Jaffe, and S. Farsiu
ARVO Annual Meeting, Vancouver, BC, April 2019 (Poster)

Deep Learning-Based Automatic Segmentation of Stromal Infiltrates and Associated Biomarkers on Slit-Lamp Images of Microbial Keratitis
S. Farsiu, J. Loo, M. F. Kriegel, M. Tuohy, V. Prajna, and M. A. Woodward
ARVO Annual Meeting, Vancouver, BC, April 2019 (Poster)

Reliability of Physicians’ Measurements When Manually Annotating Images of Microbial Keratitis
M. F. Kriegel, J. Loo, V. Prajna, S. Farsiu, M. Tuohy, P. M. Gompa, L. Niziol, and M. A. Woodward
ARVO Annual Meeting, Vancouver, BC, April 2019 (Poster)

Deep Learning Retinal OCT Analysis Reliably Predicts the Outcome of a Real-World Clinical Trial
J. Loo, T. E. Clemons, E. Y. Chew, M. Friedlander, G. J. Jaffe, and S. Farsiu
SPIE Ophthalmic Technologies XXIX, San Francisco, CA, February 2019 (Podium)

2018

Deep Learning-Based Automatic Segmentation of Ellipsoid Zone Defects in Optical Coherence Tomography Images of Macular Telangiectasia Type 2
J. Loo, L. Fang, D. Cunefare, G. J. Jaffe, and S. Farsiu
ARVO Annual Meeting, Honolulu, HI, April 2018 (Podium)