Transforming imaging data into computational pipelines to better and smarter health

What we do!

We develop novel computational methods to study and understand tissue micro-anatomy using spatial-omics data acquired via diverse technologies. Our method has implications in clinical diagnostics and allows for studying the fundamentals of biological systems. Currently, our major focus involves studying diabetic kidney disease in humans.

100 100 percent of our graduated Ph.D. students are placed in prominent pharma companies.

Lab Personnel

Latest works!

We published our work on the computational classification of diabetic nephropathy in the Journal of the American Society of Nephrology (JASN).  All the codes and images used for evaluation are made available to the community. 

We published our work on automated computational detection of interstitial fibrosis, tubular atrophy, and glomerulosclerosis in JASN.  All the codes, images, and trained models used for evaluation are made available to the community.

We published our work for the community on our cloud-based instance for digital pathology WSI visualization, automated segmentation, and error correction for training a semantic segmentation model. The source codedocker image, and a video overview are freely made available for the community.  

Our work integrating deep semantic segmentation and transformer models for disease progression prediction starting from digital image pixels and preserving pixel level spatial relationships between important tissue structures is available online. Great work by Ben Shickel (Assistant Professor, Medicine – Quantitative Health) and Nicholas Lucarelli.

join us!

Postdoctoral Associate position available: Job description.

Undergraduate Research Internship available: Job description.

HuBMAP Summer Internship: HuBMAP Website.


03/17/2023 The CMI Lab is excited to welcome new personnel Ahmed Naglah, Wenbin Guo, Sayat Mimar, Fatemeh Afsari, Hyunjae Jeong, Harishwar Reddy, Julio Maragall, and Daniel Giraldo.

03/15/2023 Sam Border presented his collaborative work with Farzad Fereidouni (Assistant Professor, UC Davis) on automated WSI collagen analysis for fibrosis quantification using deep-DUET at the United States and Canadian Academy of Pathology’s (USCAP’s) 112th Annual Meeting 2023, New Orleans, LA.

02/22/2023 Our work on studying reference kidney morphometry using brightfield histology and deep learning received an honorable mention poster award in SPIE Medical Imaging 2023. Congratulations Nicholas Lucarelli!

02/22/2023 We presented four abstracts at SPIE Digital & Computational Pathology 2023! 

01/12/2023 Dr. Sarder presented a talk titled “Computational Annotations of Cells in Histology using AI” at the School of Biomedical Engineering, University of British Columbia, Vancouver, Canada.

11/05/2022 Nicholas Lucarelli presented two works including one on fusing brightfield histology and spatial-omics data at ASN Kidney Week 2022, Orlando, FL.

11/03/2022 Dr. Sarder presented a talk titled “Computational Segmentation and Quantification in Diabetic Nephropathy,” at ASN Kidney Week 2022, Orlando, FL.