Siyavash Shabani

PhD Student & Deep Learning Researcher

Department of Electrical and Biomedical Engineering
University of Nevada, Reno
Nevada, USA

Email: sshabani@unr.edu

Biography

I am a second-year PhD student under the supervision of Prof. Bahram Parvin. My research focuses on deep learning at the intersection of machine learning, computer vision, and medical image analysis. I have a primary focus on the following topics:
1- Few-shot learning for medical image analysis

2- Transformers for medical image segmentation

3- Prediction genomic signitures from from tumor histology sections.

4- Developing new loss functions as morphometric feature-aware assistants (e.g., principal curvature) for deep learning models.

Before joining UNR, I was a volunteer research assistant at University of Hong Kong(HKU), working with Prof. Mohamad Koohi-Moghadam. I obtained my M.Sc. degree at , Amirkabir University of Technology in February 2020.

I am always open to research collaboration. So if you are interested in healthcare, machine learning, computer vision, and medical image processing, feel free to drop me an email.

Experience

University of Nevada, Reno, USA

2023- Present

Graduate Research and Teaching Assistant
Topic: Medical Image Analysis using Deep Learning
Keysight Technologies, Santa Rosa, California, USA

Summer 2025

Computer Vision and Machine Learning Intern
Topic: High-Frequency Signal (IQ/Spectrogram) Reconstruction Using U-Net, UNETR, Fourier Neural Operator (FNO)
University of Hong Kong, Clinical AI Group, Hong Kong

2021 – 2022

Research Assistant
Topic: Generative Models in Medical Image Processing
Amirkabir University of Technology, Tehran, Iran

2017. 2020

Master of Medical Information.
Topic: Cellular Interaction Analysis

Selected Publications [Google Scholar]

	MACA-Net: Multi-aperture curvature aware network for instance-nuclei segmentation Siyavash Shabani,Sahar Mohammad ,Muhammad Sohaib, Bahram Parvin Biomedical Signal Processing and Control Journal, 2026. [PDF] [Code]
	A Novel 3D Decoder with Weighted and Learnable Triple Attention for Segmentation of 3D Microscopy Images Siyavash Shabani,Sahar Mohammad ,Bahram Parvin CVPR Conference, 2025. [PDF] [Code]
	Coupled Swin Transformers and Multi-Apertures Network(CSTA-NET) Improves Medical Image Segmentation Siyavash Shabani, Muhammad Sohaib, Sahar Mohammad, Bahram Parvin 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). [PDF] [Code]
	Logsage: Log-Based Saliency for Guided Encoding in Robust Nuclei Segmentation of Immunofluorescence Histology Images Sahar Mohammad, Siyavash Shabani, Muhammad Sohaib, Bahram Parvin 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). [PDF]
	3D-Organoid-SwinNet: High-content profiling of 3D organoids Muhammad Sohaib, Siyavash Shabani, Bahram Parvin IEEE BHI Conference, 2024. [PDF] [Code]
	Multi-Aperture Fusion of Transformer-Convolutional Network(MFTC-Net) for 3D Medical Image Segmentation and Visualization Siyavash Shabani, Muhammad Sohaib, Bahram Parvin Workhop of T4V in CVPR Conference, 2024. [PDF] [Code]
	Self-supervised Region-aware Segmentation of COVID-19 CT images using 3D GAN and contrastive learning. Siyavash Shabani, Morteza Homayounfar, Mohammad Kohi-moghadam, Vardhanabhuti. Journal of Computers in Biology and Medicine 2022. [PDF] [Project page] [Code]
	The impact of recency and adequacy of historical information on sepsis predictions using machine learning. Manaf Zargoush, Alireza Sameh ,Javadi, Siyavash Shabani, Dan Perri Nature Scientific Reports. [PDF] [Project page]
	Ghost Package.. Siyavash Shabani., Reza Rawassizadeh. Published in R Studio, 2020. [PDF] [Project page] [Code]