Performance Assessment of a Deep Learning–Based Algorithm for Ovarian Cancer Histotyping in an Independent Data Set

Zelisse, H. S., Asadi-Aghbolaghi, M., Farahani, H., Snijders, M. L., Hooijer, G. K., Horlings, H. M., van de Vijver, M. J., et al.

American Journal of Surgical Pathology, 2026, 50(2), 179–188

KAFSTExp: Kernel Adaptive Filtering with Nyström Approximation for Predicting Spatial Gene Expression from Histology Images

Liu, H., Farahani, H., Li, X., Xie, Y., Bashashati, A.

IEEE J. Biomedical and Health Informatics, 2026, 30(3), 2203–2216

Pre-Surgical Risk Stratification of NSMP Endometrial Cancer Using AI on Biopsies

Asadi-Aghbolaghi, M., Farahani, H., Zhang, A., Darbandsari, A., Kommoss, S., Gilks, C. B., McAlpine, J., Bashashati, A.

Laboratory Investigation, 2026, 106(3), 943

HistoMILKD: A Multiple-Instance-Learning Multi-Teacher Knowledge Distillation Framework for Whole-Slide Image Classification

Mallya, M., Mirabadi, A. K., Farahani, H., Bashashati, A.

IEEE/CVF Winter Conf. on Applications of Computer Vision (WACV), 2026, 3390–3400

A Deep Learning Framework for Classification of Neuroendocrine Neoplasm Whole-Slide Images

Hadjifaradji, A., Diaz-Stewart, M., Chu, J., Farnell, D., Schaeffer, D., Farahani, H., Bashashati, A., Loree, J. M.

Cancers, 2025, 17(18), 2991

Benchmarking Histopathology Foundation Models for Ovarian Cancer Bevacizumab Treatment-Response Prediction from Whole-Slide Images

Mallya, M., Mirabadi, A. K., Farnell, D., Farahani, H., Bashashati, A.

Discover Oncology, 2025, 16(1), 1–15

Boltzmann Semantic Score: A Semantic Metric for Evaluating Large Vision Models Using Large Language Models

Khajegili Mirabadi, A., Rich, K., Farahani, H., Bashashati, A.

Intl. Conf. on Learning Representations (ICLR), 2025, 101667–101692

Risk Stratification Using Deep Features in IDH-Mutant Gliomas

Rich, K., Martin, K. C., Farahani, H., Ma, C., Yip, S., Bashashati, A.

Neuro-Oncology Advances, 2025, 7(Suppl 3), iii8

OCEAN Challenge: Advancing AI for Generalized Ovarian Cancer Diagnosis

Asadi-Aghbolaghi, M., Farahani, H., Zhang, A., Khajegili Mirabadi, A., Akbari, A., Kim, S., Ramus, S., Köbel, M., et al.

Laboratory Investigation, 2025, 105(3), 941

Machine Learning-Driven Histotype Diagnosis of Ovarian Carcinoma: Insights from the OCEAN AI ChallengePreprint

Asadi-Aghbolaghi, M.*, Farahani, H.*, Zhang, A.*, Akbari, A., Kim, S., Chow, A., Dane, S., Huntsman, D. G., Gilks, C. B., Ramus, S., Köbel, M., Karnezis, A., Bashashati, A.

medRxiv, 2024.04

Learning Generalizable AI Models for Multi-Center Histopathology Image Classification

Asadi-Aghbolaghi, M., Darbandsari, A., Zhang, A., Contreras-Sanz, A., Boschman, J., Ahmadvand, P., Köbel, M., Farnell, D., Huntsman, D. G., Churg, A., Black, P., Wang, G., Gilks, C. B., Farahani, H., Bashashati, A.

npj Precision Oncology, 2024, 8(1), 151

A Deep Learning Approach for the Identification of the Molecular Subtypes of Pancreatic Ductal Adenocarcinoma Based on Whole-Slide Pathology Images

Ahmadvand, P.*, Farahani, H.*, Farnell, D.*, Darbandsari, A., Topham, J., Karasinska, J., Nelson, J., Naso, J., Jones, S. J., Renouf, D., et al.

American Journal of Pathology, 2024, 194(12), 2302–2312

Benchmarking Bulk and Single-Cell Variant-Calling Approaches on Chromium scRNA-Seq and scATAC-Seq Libraries

Wiens, M., Farahani, H., Scott, R. W., Underhill, T. M., Bashashati, A.

Genome Research, 2024, 34(8), 1196–1210

GRASP: Graph-Structured Pyramidal Whole-Slide Image Representation

Khajegili Mirabadi, A., Archibald, G., Darbandsari, A., Contreras-Sanz, A., Nakhli, R., Asadi, M., Zhang, A., Black, P., Gilks, C. B., Wang, G., Farahani, H., Bashashati, A.

AAAI Conf. on Artificial Intelligence, 2024

Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-Pixel Images

Nakhli, R., Moghadam, P. A., Mi, H., Farahani, H., Baras, A., Gilks, C. B., Bashashati, A.

IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), 2023, 11547–11557

CO-PILOT: Dynamic Top-Down Point Cloud with Conditional Neighborhood Aggregation for Multi-Gigapixel Histopathology Image Representation

Nakhli, R., Zhang, A., Mirabadi, A., Rich, K., Asadi, M., Gilks, C. B., Farahani, H., Bashashati, A.

IEEE/CVF Intl. Conf. on Computer Vision (ICCV), 2023, 21063–21073

ALL-IN: A Local-Global Graph-Based Distillation Model for Representation Learning of Gigapixel Histopathology Images with Application in Cancer Risk Assessment

Azadi, P., Suderman, J., Nakhli, R., Rich, K., Asadi, M., Kung, S., Oo, H., Keyes, M., Farahani, H., MacAulay, C., et al.

Intl. Conf. on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, 2023, 765–775

A Morphology-Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images

Moghadam, P. A., Van Dalen, S., Martin, K. C., Lennerz, J., Yip, S., Farahani, H., Bashashati, A.

IEEE/CVF Winter Conf. on Applications of Computer Vision (WACV), 2023, 2000–2009

CCRL: Contrastive Cell Representation Learning

Nakhli, R., Darbandsari, A., Farahani, H., Bashashati, A.

Computer Vision – ECCV 2022 Workshops, Springer, 2023, 397–407

Deep Learning-Based Histotype Diagnosis of Ovarian Carcinoma Whole-Slide Pathology Images

Farahani, H., Boschman, J., Farnell, D., Darbandsari, A., Zhang, A., Ahmadvand, P., Jones, S. J., Huntsman, D., Köbel, M., Gilks, C. B., et al.

Modern Pathology, 2022, 35(12), 1983–1990

The Utility of Color Normalization for AI-Based Diagnosis of Hematoxylin- and Eosin-Stained Pathology Images

Boschman, J.*, Farahani, H.*, Darbandsari, A., Ahmadvand, P., Van Spankeren, A., Farnell, D., Levine, A. B., Naso, J. R., Churg, A., Jones, S. J., Yip, S., Köbel, M., Huntsman, D. G., Gilks, C. B., Bashashati, A.

Journal of Pathology, 2022, 256(1), 15–24

Identification of a Novel Subtype of Endometrial Cancer with Unfavorable Outcome Using AI-Based Histopathology Image Analysis

Darbandsari, A., Farahani, H., Abolmaesumi, P., Leung, S., Kommoss, S., Huntsman, D., Talhouk, A., Gilks, C. B., McAlpine, J. N., Bashashati, A.

ASCO Annual Meeting, 2022

Deep Learning-Based Classification Distinguishes Sarcomatoid Malignant Mesotheliomas from Benign Spindle-Cell Mesothelial Proliferations

Naso, J. R., Levine, A. B., Farahani, H., Chirieac, L. R., Dacic, S., Wright, J. L., Lai, C., Yang, H.-M., Jones, S. J., Bashashati, A., et al.

Modern Pathology, 2021, 34(11), 2028–2035

Synthesis of Diagnostic-Quality Cancer Pathology Images by Generative Adversarial Networks

Levine, A. B., Peng, J., Farnell, D., Nursey, M., Wang, Y., Naso, J. R., Ren, H., Farahani, H., Chen, C., Chiu, D., et al.

Journal of Pathology, 2020, 252(2), 178–188

Classification of Epithelial Ovarian Carcinoma Whole-Slide Pathology Images Using Deep Transfer Learning

Wang, Y., Farnell, D., Farahani, H., Nursey, M., Tessier-Cloutier, B., Jones, S. J., Huntsman, D. G., Gilks, C. B., Bashashati, A.

Medical Imaging with Deep Learning (MIDL), 2020

clonealign: Statistical Integration of Independent Single-Cell RNA and DNA Sequencing Data from Human Cancers

Campbell, K. R., Steif, A., Laks, E., Zahn, H., Lai, D., McPherson, A., Farahani, H., Kabeer, F., Biele, J., et al.

Genome Biology, 2019, 20, 1–12

Engineered In-Vitro Cell-Line Mixtures and Robust Evaluation of Computational Methods for Clonal Decomposition and Longitudinal Dynamics in Cancer

Farahani, H.*, de Souza, C. P.*, Billings, R., Yap, D., Shumansky, K., Wan, A., Lai, D., Mes-Masson, A.-M., Aparicio, S., Shah, S. P.

Scientific Reports, 2017, 7(1), 13467

Multifocal Endometriotic Lesions Associated with Cancer Are Clonal and Carry a High Mutation Burden

Anglesio, M. S., Bashashati, A., Wang, Y. K., Senz, J., Ha, G., Yang, W., Aniba, M. R., Prentice, L. M., Farahani, H., et al.

Journal of Pathology, 2015, 236(2), 201–209

Learning Bounded Tree-Width Bayesian Networks Using Integer Linear Programming

Parviainen, P., Farahani, H., Lagergren, J.

Artificial Intelligence and Statistics (AISTATS), PMLR, 2014, 751–759

Learning Oncogenetic Networks by Reducing to Mixed Integer Linear Programming

Farahani, H., Lagergren, J.

PLoS ONE, 2013, 8(6), e65773

A-to-I Editing of microRNAs in the Mammalian Brain Increases During Development

Ekdahl, Y.*, Farahani, H.*, Behm, M., Lagergren, J., Öhman, M.

Genome Research, 2012, 22(8), 1477–1487

billiARds: Augmented Reality System with Wearable Force-Feedback Device

Sargaana, U., Farahani, H., Lee, J., Ryu, J., Woo, W.

Intl. Conf. on Human-Computer Interaction (HCII), 2005

Robust Adaptive Control Simulation of a Wire-Suspended Parallel Manipulator

Farahani, H., Kim, B.-H., Ryu, J.-H.

Intl. Conf. on Control, Automation and Systems (ICCAS), 2004, 46–51