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ABOUT US
Vysioneer, founded by Massachusetts General Hospital (MGH) Scientists and Massachusetts Institute of Technology (MIT) alumni, aims for precision medicine with deep learning-based techniques. We have built partnerships with leading cancer centers in Asia and the US. We have the best technical, clinical, and business expertise in the team. We are Vysioneer, visionary & pioneer for the best medicine.
OUR SERVICE
Unlocking AI for healthcare
MODEL DEPLOYMENT INTO CLINICAL WORKFLOW
Vysioneer uses deep learning technology to empower image analysis in real time to assist clinicians in workflow and to improve quality of care. Our solution is PACS-vendor neutral and can be integrated seamlessly into your existing health system infrastructure, helping clinicians leverage their existing PACS and reporting systems to create higher value reports faster.
ONLINE IMAGE ANALYSIS SERVICE
On demand image analysis through a cloud platform that enables receiving imaging scans from various modalities and automatic image analysis for different clinical findings. Results are provided in real time to medical professionals as needed.
CAREERS
We're hiring!
MACHINE LEARNING SCIENTIST
Taipei, Taiwan
Machine Learning (ML) team is a core component of Vysioneer to develop and validate machine learning models for precision medicine. Responsibilities include:
Develop deep learning-based models that analyze medical imaging data and automate clinical workflow
Work closely with clinicians to design product features that address the most critical clinical problems
Test and integrate the algorithm/model in the production environment
Publishing the results of work at top-tier machine learning and clinical conferences
FULL-STACK SOFTWARE ENGINEER
Taipei, Taiwan
Software Engineering team is a core component of Vysioneer to deploy models in the hospital setting to facilitate clinical practice. Responsibilities include:
Creating tools and pipelines to route data and results to hospital systems
Building visualization tools to demonstrate AI results in front of the clinicians