Project overview
Computational Pathology AI Lab is a production-oriented, research-first platform for histopathology image analysis, cancer-detection research, explainable deep learning, uncertainty estimation, whole-slide workflows, model evaluation, and reproducible MLOps. It began as a University of Colorado Boulder graduate-course project and has been expanded into a maintainable engineering and academic portfolio.
Four professional pillars
Medical AI Research
Leakage controls, calibration, uncertainty, confidence intervals, error analysis, domain shifts, and external-validation readiness.
ML Engineering and MLOps
Typed Python package, tests, CI, Docker, DVC, MLflow hooks, model-registry structure, API serving, and monitoring.
Computer Vision Engineering
CNNs, transfer learning, Vision Transformers, foundation-model adapters, tissue patches, WSI utilities, MIL, and explainability.
Academic Teaching and Authorship
Teaching notebooks, methodology, model and data cards, research questions, a technical report, and a companion book.
Permanent identifiers
Software — all versions
Archived software — version 1.0.0
Companion book
10.5281/zenodo.21444837GGKEY:8ZWNQ7NFGBL
Evidence status
CPU smoke tests, synthetic-data workflows, automated tests, CI, Docker build, and GitHub Pages deployment are operational. Real histopathology benchmarks, external validation, foundation-model comparisons, and clinical validation are explicitly Not Yet Benchmarked or Not Performed.
Companion book
Computational Pathology Engineering
From Histology Patches and CNNs to Whole-Slide Foundation Models, Explainable AI, and Production MLOps
Author: Faramarz Kowsari
DOI: 10.5281/zenodo.21444837
Google Books Key: GGKEY:8ZWNQ7NFGBL
About the author
Faramarz Kowsari
Faramarz Kowsari is an author, Software Engineer and AI researcher based in Istanbul. Focusing on the intersection of technology, education, and personal growth, he has published over 80 digital titles on international platforms. His areas of expertise span Artificial Intelligence, prompt engineering, modern trading strategies (Smart Money Concepts & algorithmic trading), as well as classical literature and mindfulness. In addition to writing, he develops web-based educational tools and creates specialized instructional video content.