Lee Cooper

Northwestern University — United States

Computer Science · Physical Sciences

51h-index17.1kcitations253works2.52yr avg
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Research Topics

AI in cancer detection(73), Radiomics and Machine Learning in Medical Imaging(55), Cell Image Analysis Techniques(43), Glioma Diagnosis and Treatment(40), Cancer Genomics and Diagnostics(28)

Publications250 total

HiFusion: Hierarchical Intra-Spot Alignment and Regional Context Fusion for Spatial Gene Expression Prediction from Histopathology
arXiv (Cornell University)·2025Open Access
IMG-121. BraTS-Pathology 2024: Insights and Future Directions Informed by the AI-RANO & RANO-RGP Effort to Assess Glioblastoma Heterogeneity
Neuro-Oncology·2025Open Access
Artificial Intelligence (AI)-Driven Screening of Equivocal Prostate Immunohistochemistry (IHC) Cases: Development and Validation of a Screening and Cancer Detection Framework
Modern Pathology·2025Open Access
Learning from Gene Names, Expression Values and Images: Contrastive Masked Text-Image Pretraining for Spatial Transcriptomics Representation Learning
arXiv (Cornell University)·2025Open Access
Development of an Automated Tumor Spectral Artifact Removal Algorithm Using Active Learning
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition·2025
Personalized brain tumor radiation therapy planning pipeline based on spectroscopic MRI using the Brain Imaging Collaboration Suite (BrICS)
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition·2025
A Self-Supervised Deep Learning Method for Accelerated Quantification of High-Resolution Short-TE spectroscopic MRI Datasets
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition·2025
2510P Digital pathology prediction of luminal subtype and hormonal therapy responsiveness of prostate cancer patients
Annals of Oncology·2025
HistomicsTK: A Python toolkit for pathology image analysis algorithms
SoftwareX·2025· 6 citedOpen Access
Artificial Intelligence (AI)-Powered H&E Whole-Slide Image Analysis of Tertiary Lymphoid Structure (TLS) Independently Predicts Survival in Patients with Non-Small Cell Lung Cancer (NSCLC) Receiving Immunotherapy
2025Open Access
Explainable Cryobiopsy AI Model, CRAI, to Predict Disease Progression for Transbronchial Lung Cryobiopsies with Interstitial Pneumonia
2025Open Access
Artificial intelligence-based digital pathology using H&E-stained whole slide images in immuno-oncology: from immune biomarker detection to immunotherapy response prediction
Journal for ImmunoTherapy of Cancer·2025· 8 citedOpen Access
Sparser2Sparse: Single-shot Sparser-to-Sparse Learning for Spatial Transcriptomics Imputation with Natural Image Co-learning
arXiv (Cornell University)·2025Open Access
Informatics at the Frontier of Cancer Research
Cancer Research·2025Open Access
Deep learning for fetal inflammatory response diagnosis in the umbilical cord
Placenta·2025
Benchmarking pathology foundation models for non-neoplastic pathology in the placenta
2025· 1 citedOpen Access
Tumor-infiltrating lymphocytes in breast cancer through artificial intelligence: biomarker analysis from the results of the TIGER challenge
2025Open Access
A0932 – Clinical and reader associated correlates of clinically significant prostate cancer invisible on multiparametric MRI
European Urology·2025
An Artificial Intelligence–Digital Pathology Algorithm Predicts Survival After Radical Prostatectomy From the Prostate, Lung, Colorectal, and Ovarian Cancer Trial
The Journal of Urology·2025· 5 cited
An Accelerated Spectroscopic MRI Metabolite Quantification Based on a Deep Learning Method for Radiation Therapy Planning in Brain Tumor Patients
Cancers·2025· 1 citedOpen Access
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Frequent Co-authors

Daniel J. Brat(20), David A. Gutman(19), Carlos S. Moreno(14), Joel Saltz(12), Mohamed Amgad(11), Jun Kong(11), Tahsin Kurç(8), Tom Mikkelsen(7), Erwin G. Van Meir(7), Fusheng Wang(7), Ashish Sharma(7), Jill S. Barnholtz‐Sloan(5), Andrei A. Ivanov(5), Xiulei Mo(5), Yuhong Du(5), Haian Fu(5), Tony Pan(5), Jason T. Huse(4), Laila Poisson(4), Adam E. Flanders(4)