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portfolio

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publications

Artificial intelligence breakthroughs in pioneering early diagnosis and precision treatment of breast cancer: A multimethod study

Published in European Journal of Cancer, 2024

This is a systematic multimethod study that evaluates the effectiveness of various AI and DL techniques in the early detection and precision treatment of breast cancer. Future efforts in the generalization of these models across diverse populations and encourage cross-disciplinary collaboration to integrate AI tools more seamlessly into standard clinical workflows.

Recommended citation: Darbandi, Mohammad Reza, et al. ""Artificial intelligence breakthroughs in pioneering early diagnosis and precision treatment of breast cancer: A multimethod study". "European Journal of Cancer. 209 (2024): 114227.
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Advancements in AI for Breast Cancer Diagnosis: A 2024–2025 Systematic Review Update

Published in AI Revolution: Research, Ethics and Society, 2025

This paper is mainly about a systematic review of 285 recent articles focusing on the transformative role of artificial intelligence in breast cancer research between 2024 and 2025. The future work is centered on addressing continuing challenges in the field to further integrate these AI techniques into clinical practice for improved patient care.

Recommended citation: Darbandi, Mohammad Reza, et al. "Advancements in AI for Breast Cancer Diagnosis: A 2024–2025 Systematic Review Update." International Conference on the AI Revolution. Cham: Springer Nature Switzerland, 2025
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Bridging Brains and Machines: A Unified Frontier in Neuroscience, Artificial Intelligence, and Neuromorphic Systems

Published in ArXiv, 2025

This paper is mainly about the emerging convergence of neuroscience, AGI, and neuromorphic computing.The future work is focused on scaling neuromorphic systems to match the complexity of the human brain.

Recommended citation: Shankar, S., Pan, Y., Jiang, H., Liu, Z., Darbandi, M. R., Lorenzo, A., ... & Liu, T. (2025)."Bridging brains and machines: A unified frontier in neuroscience, artificial intelligence, and neuromorphic systems. " arXiv preprint arXiv:2507.10722.
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Foundation Models as Data Engines:Label-Efficient Learning in Modern Computer Vision

Published in AI Revolution: Research, Ethics and Society, 2026

This paper introduces a supervision-centered taxonomy that explores how models like SAM and CLIP are used to automatically curate, label, and distill knowledge into efficient student models. This roadmap aims to create self-evolving AI systems that can democratize visual intelligence by autonomously improving through iterative data verification and self-improvement cycles.

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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Nima Darbandi