BMQuark LLC helps research labs, biotech and healthcare organizations extract reliable results from medical images and biomedical data — faster, and with the rigor of NIH-grade research.
Biomedical imaging and data teams often hit the same walls. We remove them.
Your team has great biology or clinical knowledge, but lacks deep learning and computer vision specialists to build reliable image analysis pipelines.
Segmenting tumors, organs, or cells by hand across hundreds of scans eats months of researcher time and introduces inconsistency.
Raw imaging and multi-modal datasets (CT/PET/MRI, pathology slides, omics) pile up without a reproducible pipeline to clean, align, and analyze them.
Quick-and-dirty scripts don't survive peer review or regulatory scrutiny. You need methods that are validated, documented, and reproducible.
End-to-end biomedical data and imaging services, built on a decade of academic and NIH research experience.
Automated, deep-learning-based segmentation and co-registration for CT, PET-CT, MRI and other multi-modal imaging data.
Purpose-built computer vision models for classification, detection, and quantitative analysis of biomedical images.
Scalable, reproducible pipelines to clean, curate, and structure large imaging and biomedical datasets.
Hands-on collaboration to design analysis strategies, troubleshoot methods, and support publication-ready results.
BMQuark LLC was founded by Dr. Zisha Zhong, who serves as core developer and lead scientist. Dr. Zhong holds a Ph.D. in Pattern Recognition and Artificial Intelligence from the Institute of Automation, Chinese Academy of Sciences, with a research focus on image processing, image analysis, and computer vision.
Between 2022 and 2025, Dr. Zhong conducted postdoctoral research at the National Institutes of Health (NIH), applying machine learning to biomedical data analysis. Prior work has included PET-CT tumor co-segmentation, deep learning-based cancer treatment response prediction, and image segmentation research published in venues such as Medical Physics and IEEE ISBI.
That combination of academic rigor and applied biomedical experience is what BMQuark brings to every client project.
A look at a system Dr. Zhong built and led during his NIH postdoctoral research — now live in production.
Center for Cancer Research, Cancer Data Science Lab (Jiang Lab)
Pathologists and researchers had no fast way to search terabyte-scale H&E slide archives by image similarity — the pathology-image equivalent of a sequence search tool like BLAST simply didn't exist at this scale.
Built an AI-based image encoding pipeline paired with a hierarchical skip-indexing structure, enabling searches across multiple tumor histopathology cohorts (NCI Lab of Pathology, TCGA, CPTAC) from a single desktop-class machine — no specialized infrastructure required.
Retrieval accuracy validated through blinded scoring by practicing pathologists, outperforming existing image retrieval tools. The system also links image features to gene expression and molecular pathways via spatial transcriptomics — and is live in production today.
This project was developed by Dr. Zhong in his official capacity as a postdoctoral researcher at the National Cancer Institute (NCI), National Institutes of Health (NIH), and is presented here for informational purposes to describe his prior research experience. Its inclusion on this page does not imply endorsement, sponsorship, or affiliation of BMQuark LLC by NIH, NCI, or the U.S. Government.
A simple, transparent process from first conversation to delivered results.
We learn about your data, goals, and constraints.
Clear deliverables, timeline, and pricing.
Regular check-ins and progress updates.
Validated results plus documentation and follow-up support.
Whether it's a single dataset or an ongoing research partnership, we'd love to hear what you're working on.
contact@bmquark.com