Nima Darbandi
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Pages
Posts
slidesMerge Update: Config-Driven Parameters, Manual CC Selection, and Better Diagnostics
Published:
I pushed a new version of slidesMerge with several practical improvements based on real-world use. The core pipeline (coarse alignment, contour refinement, tiled warping, multi-channel merge) is unchanged. What changed is adding a centralized configuration file, improving edge-case handling in tissue detection, and improving resilience across diverse immunofluorescent slides with selectable image features.
Patient Co-Pilot v2: Multi-AI, Apple Health Monitoring, Source-grounded ChatBot
Published:
I am publishing Patient Co-Pilot v2 Beta, a practical update focused on Multi-AI support and Symptom Monitoring. Download link for the Beta version is included.
Patient Co-Pilot: Helping Patients Navigate Complex Care with Confidence
Published:
I built Patient Co-Pilot mobile app to solve a common patient problem: confused with too much medical information, spread across too many places, from too many doctors.
Paper Accepted at AIR-RES 2026
Published:
I am pleased to share that our paper has been accepted at the 2026 International Conference on the AI Revolution: Research, Ethics, and Society (AIR-RES 2026) for both publication in the proceedings and conference presentation.
ReadingOverlayTool: A Swift macOS Reading Line Overlay for PDFs
Published:
I developed ReadingOverlayTool to make long PDF reading sessions on macOS easier and less fatiguing. Super useful for putting less strain on eyes while reading long papers in any room lighting conditions.
Honored to Receive the 2026 POOIA Student Scholarship Award for Cancer Research
Published:
I am grateful to share that I earned the 2026 POOIA Student Scholarship Award.
Started a Graduate Research Assistantship Position in ISP Lab
Published:
I am excited to announce that I have been accepted as a Research Assistant in ISP Lab for a two-year term, starting January 12, 2026 (Spring 2026).
slidesMerge: Microscopy Slide Alignment and 14-Channel OME-TIFF Fusion
Published:
I developed slidesMerge as a practical pipeline to align two large Aivia microscopy slides (>7 channels each) from different imaging cycles and fuse them into a single multi-channel OME-TIFF file.
CD73 KO vs WT RNA-seq Analysis with Kallisto and Sleuth
Published:
I built this project to run a reproducible RNA-seq comparison between CD73 knockout (KO) and wild-type (WT) samples using a practical Kallisto + Sleuth workflow.
CheXray: Multi-Model Chest X-ray Analysis and Report Drafting
Published:
I developed CheXray as a research prototype to combine computer vision models and an LLM for chest X-ray interpretation support.
Background and Artifact Removal for Multiplex Tissue Imaging
Published:
I built this project to improve background and artifact removal in multiplex pathology images, including whole-slide imaging (WSI) and multi-channel microscopy data.
Paper Published at European Journal of Cancer
Published:
Our review article AI for Early Diagnosis and Precision Treatment of Breast Cancer has been published in the European Journal of Cancer.
Honored to Receive UGA School of Computing Outstanding TA Award
Published:
I am honored to share that I received the UGA Outstanding Teaching Assistant Award (Spring 2024) from the School of Computing.
portfolio
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Portfolio item number 2
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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.
Download Paper | Download Bibtex
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
Download Paper | Download Bibtex
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.
Download Paper | Download Bibtex
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.
Recommended citation: Darbandi, Mohammad Reza, et al. "Foundation Models as Data Engines:Label-Efficient Learning in Modern Computer Vision. " International Conference on the AI Revolution. Cham: Springer Nature Switzerland, 2026
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talks
Talk 1 on Relevant Topic in Your Field
Published:
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Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
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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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