Rohban, Mohammad

Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl

Caicedo, J. C., Goodman, A., Karhohs, K. W., Cimini, B. A., Ackerman, J., Haghighi, M., Heng, C., Becker, T., Doan, M., McQuin, C., et al. (2019) · Nature Methods

Cell Image Analysis TechniquesDigital Imaging for Blood Diseases

Cited by 809

Data-analysis strategies for image-based cell profiling

Caicedo, J. C., Cooper, S., Heigwer, F., Warchal, S., Qiu, P., Molnar, C., Vasilevich, A. S., Barry, J. D., Bansal, H. S., Kraus, O., et al. (2017) · Nature Methods

Cell Image Analysis TechniquesSingle-cell and spatial transcriptomics

Cited by 739

A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges

Salehi, M., Mirzaei, H., Hendrycks, D., Li, Y., Rohban, M. H., & Sabokrou, M. (2021) · arXiv

Anomaly Detection Techniques and ApplicationsAdversarial Robustness in Machine Learning

Cited by 118

Notice of Removal: Multiresolution Knowledge Distillation for Anomaly Detection

Salehi, M., Sadjadi, N., Baselizadeh, S., Rohban, M. H., & Rabiee, H. R. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Anomaly Detection Techniques and ApplicationsAdversarial Robustness in Machine Learning

Cited by 97

The Silent Judge: Unacknowledged Shortcut Bias in LLM-as-a-Judge

Marioriyad, A., Rohban, M. H., & Baghshah, M. S. (2025) · arXiv

Topic ModelingEthics and Social Impacts of AI

Cited by 0