An AI-based decision support solution for diagnosis and prognostication of prostate cancer
CADESS™ will help determine who can live with prostate cancer and who requires surgery to survive
CADESS Medical was founded in 2016 with the mission to improve patient outcome and reduce healthcare costs for prostate cancer by delivering innovative digital pathology decision support solutions.
We offer CADESS™ - an AI-based decision support system for hospital and commercial pathology laboratories involved in diagnosis and prognostication of prostate cancer.
CADESS™ is based on digital state-of-the-art imaging analysis software technology and helps determine who can live with prostate cancer and who requires aggressive treatment to survive
- CADESS™ offers improved diagnostic accuracy resulting in reduced patient suffering and lower treatment costs.
- Fast and accurate identification of malignant glands in tissue images helps pathologists determine the aggressiveness of the cancer.
- CADESS™ is based on the collective opinion of thirteen internationally prominent pathologists.
- First version offered as cloud-based pay-per-use service solution.
CADESS™ is a cloud-based decision support system based on mature technology
CADESS™ runs on a cloud GPU-platform infrastructure to enable cost-effective and scalable software as a service. OpenSeaDragon gives an interface similar to that of GoogleMaps, with efficient pan and zoom which pathologists expect from their experience with microscopes. The client devices may use any modern browser.
CADESS™ is based on ten years of solid research at Uppsala University
Our stain is absorbed by the connective tissue surrounding the glands in the prostate and identifies the boundaries of these glands.
A tissue database consensus-graded by 13 prominent pathologists in seven countries.
Advanced image analysis techniques are used to segment the prostate glands from the surrounding tissue.
Features in the prostate glandular architecture known to be linked to cancer are extracted from the tissue data.
Features extracted by deep learning techniques complement the known types of features.
A sophisticated classifier has been trained on the glandular features in the consensus-graded database.
Meet the CADESS™ team
Ingrid Carlbom, PhD
25 years of research management at US research labs: Schlumberger-Doll Research, Digital Equipment Corporation, and Lucent’s Bell Laboratories. PhD in computer science from Brown University. Professor Emeritus, Department of Information Technology, Uppsala University.
Christophe Avenel, PhD
Three years of post-doctoral work at Uppsala University. PhD in computer science from Université Rennes. MS in computer science from Ecole Normale Supérieure de Cachan.
Anna Tolf, MD
30 years experience in surgical pathology, 16 years as a uropathologist. Senior consultant at Karolinska University Hospital, Stockholm and currently at Uppsala University Hospital, Uppsala.
Anca Dragomir, MD, PhD
Pathologist at the Uppsala University Hospital; special interest in urologic pathology. PhD in cell biology from Uppsala University, Sweden. 20 years research experience in cell biology at Uppsala University.
Dag Hammarskjölds väg 34A
SE-752 37 Uppsala
|+46 (0) 73 048 20 25|
North America regional office:
|CADESS Medical regional office
21 Oakley Ave
Summit, NJ 07901