Oncospace Announces collaboration agreement with University of Washington

ANNOUNCEMENT
11 JUL 2023
Oncospace Inc. announces collaboration agreement with University of Washington
Oncospace Announces Channel Partner Agreement with schneider & schulte

ANNOUNCEMENT
11 JUL 2023
Oncospace has entered into a channel partnership agreement with PARTNER
Oncospace Announces Channel Partner Agreement with Tecnologie Avanzate

ANNOUNCEMENT
6 JUN 2023
Oncospace Inc. announces a channel partner agreement with Tecnologie Avanzate
Oncospace Announces Data Conversion for Philips Pinnacle

ANNOUNCEMENT
6 JUNE 2023
Oncospace Inc. announces the launch of a new offering: Oncospace Patient Data Conversion
BALTIMORE, 6 JUN 2023
Oncospace, Inc. today announced the launch of Patient Data Conversion Service
Oncospace Practical Big Data Workshop Presentations 2023

ANNOUNCEMENT
15 MAY 2023
Oncospace congratulates presents latest accomplishments at the Practical Big Data Workshop
FDA Clearance for Head and Neck support in Predictive Planning Received

ANNOUNCEMENT
3 FEB 2023
FDA Clearance for Head and Neck support in Predictive Planning Received
Oncospace Inc. Expands Predictive Planning with Support for Head and Neck

PRESS RELEASE
14 OCTOBER 2022
Oncospace expands clinical application of their cloud-based radiotherapy planning tools with support for Head and Neck Predictive Planning.
Innovation in health and life sciences with Microsoft for Startups

ANNOUNCEMENT
26 AUGUST 2021
Oncospace is honored to be a part of Microsoft of Startups program.
Prize-winning AI research, bringing machine learning tools into the radiotherapy clinic

ANNOUNCEMENT
23 JUNE 2022
In this podcast, Oncospace Co-founder and Chief Scientist, Todd McNutt, Ph.D., shares his view of using artificial intelligence and machine learning to improve our understanding of the impact of radiation therapy on patients.
Machine learning makes its mark on medical imaging and therapy

ANNOUCEMENT
24 MARCH 2022
In this article Oncospace collaborator, Qiongge Li from Johns Hopkins University School of Medicine discusses an innovative anomaly detection algorithm designed to increase patient safety.