Better and faster cancer diagnosis
The purpose of the project is to innovate in the field of medical imaging.
Today the hospitals and diagnostic centers experience difficulties in making the proper diagnosis based on the image data from histopathology, MRI or PET. First, the distinction between cancer grades is difficult to estimate and different doctors can have different opinions, second, there are technical challenges in sharing and analyzing the images in a cost-effective manner.
Our mission is to leverage our cutting-edge algorithms to create a sharing platform for doctors and patients to pre-diagnose the histopathology, MRI and PET images automatically. This way the diagnosis could be made quicker and at lower cost. We have competences and experience to accomplish this goal. Our software was already awarded the first price: at MICCAI 2015 for “Combined Imaging and Digital Pathology Primary Tumor Classification”, and at MICCAI 2016 for segmentation of PET images. Our software is currently used at Lower Silesian Center of Oncology and evaluated at the Universitätsklinikum Carl Gustav Carus in Dresden in the field of histopathology and at the University of Edinburgh in MRI Imaging.
Our goal is to build the medical platform for sharing medical images with other medical experts and pre-diagnose them with our award-winning algorithms. There will be other new algorithms (open source and commercial).
This platform will help people and other physicians to get the second opinion about their image data and pre-evaluate the diagnosis before the real one is made.
In the future, our platform will be offer to medical universities - tool to learn new physicians in Medical imaging (MRI, PET, Pathology, others)
Because of the high level of data sensitivity, we will be using already existing cloud security platforms such as IBM Watson Health or Microsoft Health Vault or other. The platforms are already compliant with the privacy standards and provide a high level of security for storing the patient data. In addition, they are already recognized in the health sector.
We will lower the costs of development and infrastructure by taking advantages of storage, infrastructure and security services offered by public cloud providers. This way our platform will be scalable and provide business continuity and disaster recovery.
Our licensing model will be subscription based with different price models to guarantee the constant revenue stream.
For more information please take a look at http://cancercenter.ai/
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