Visiopharm Engages in Major Initiative for Deep Learning in Tissue Based Pathology
Hørsholm, Denmark, May 3rd – Visiopharm A/S announces the first result of their multifaceted strategy to apply Deep Learning technologies to its leading image analysis solution for cancer research and diagnostics. Visiopharm considers Deep Learning an important technological breakthrough for tissue pathology that offers the potential to make a real difference in the assessment of tissue structures, which is probably one of the most complex and challenging applications of image analysis.
Real progress in this field requires a multi-disciplinary approach and therefore Visiopharm has established a broad multidisciplinary strategy to lead further development of Deep Learning in tissue pathology, within several important clinical and research applications. To support this strategy Visiopharm has established an International Consortium for Deep Learning in Tissue Pathology. The consortium involves Academic Medical Centers, Tissue 
Biobanks, Engineering Universities, Biopharmaceutical companies, and other industrial partners who all share this vision. “We are very excited about our fast progress in deep learning over the past year and Visiopharm remains committed to investing a lot more in this technology over the coming years. We are currently formalizing the consortium and we look forward to continued collaborations and sharing more details on this initiative soon” says Michael Grunkin, CEO of Visiopharm.
Visiopharm co-supervises several Masters Students and sponsor/co-supervises industrial PhDs in collaboration with its partners in the consortium, including the Technical University of Denmark and DTU Compute, a department we have worked closely with for many years. Early results of this effort have come from Masters Student, Jeppe Thaagaard who is doing his Thesis work on Deep Learning. “We are very excited about these first results! Jeppe is only a few months into the project, and the Deep Learning algorithms developed on the Visiopharm software platform, were ranked 5th out of 23, with a marginal score difference to the winner of the CAMELYON17 competition” says CTO Johan Doré. “We look forward to a continued collaboration with Jeppe after his Thesis work, and congratulate him on his focused efforts providing such impressive results after only a few months’ work” continues CTO Johan Doré.
Visiopharm plans to make advantages of this new technology available to current users of the Oncotopix® products for cancer research and diagnostics, but also to include it in new product brands on their way to market.
The goal of the recent CAMELYON17 Deep Learning competition was to evaluate new and existing algorithms for automated detection and classification of breast cancer metastases in whole-slide images of histological lymph node sections, which are of high clinical relevance to pathologists. Full results and details of the CameLyon17 competition can be found at https://camelyon17.grand-challenge.org/results/.
About Visiopharm
Visiopharm is a leading provider of quantitative image analysis software and end-to-end Digital Pathology solutions for Hospitals, biopharmaceutical companies and research institutions around the world. Recently, Visiopharm has expanded to include ONCOTOPIX® Diagnostics a robust solution for cancer diagnostics with CE-IVD algorithms for the breast panel, integrated LIS driven workflow, and VirtualDoubleStaining™. Visiopharm’s software is featured in over 1150 scientific publications, and is compatible with leading slide scanner manufacturers, data management software, and staining providers.
Visiopharm, a privately-owned company, is an international business with over 850 licenses placed, with countless users, in more than 30 countries. A growing network of authorized distributors and integration partners support the growth of Visiopharm solutions on several continents including North America, Europe and Asia.
For further information, please contact:
Helle Fisker, CMO
Visiopharm A/S
Tel: +45 29178777
Email: hfi@visiopharm.com
