Medical Image + Deep Learning Algorithm= Faster Cancer Diagnosis with Better Efficacy
Our mission – supporting cancer diagnosis by AI (machine and deep learning)
The number of deaths from cancers worldwide is staggering—8.8 million deaths in 2015 according to the World Health Organization! In the US alone 20% of all deaths are due to cancer each year. Our team at Cancer Center is driven and motivated to help fight these various forms of cancers around the world using advanced and innovative software tools for fast and effective early diagnosis and mitigation. We hope to contribute to the fight for eradicating and preventing cancers by collaborating with medical professionals and researchers worldwide.
Using Deep Learning (DL) and Machine Learning (ML) platforms, we have developed specialized algorithmic solutions to analyze medical images for oncology and radiology for faster and better than human accuracy. Our solutions, which are both in API and Web Platforms, are designed to offer faster and better access for second and third diagnostic opinions by medical professionals to help develop a therapeutic approach quickly. This will help the patients and physicians by reducing anxiety caused by not knowing what they are dealing with.
Our solutions do all the work—segment images, find regions of interests, generate statistical descriptions of images, count cells, mitosis, and even recognize the type of cell.
Your focus—saving lives, our focus—image processing and fast detection!
- Machine Learning / Deep Learning
- Automated Medical Image Analysis
- MRI (Magnetic Resonance Imaging)
- PET / CT
- Pathology Images (Whole Slide Imaging)
- General planimetry
MRI, DICOM Analysis
In oncology, Positron Emission Tomography (PET) imaging is widely used in diagnostics of cancer metastases, in monitoring of progress in course of the cancer treatment, and in planning radiotherapeutic interventions. Accurate and reproducible delineation of the tumor in the PET scans remains a difficult task, despite being crucial for delivering appropriate radiation dose, minimizing adverse side-effects of the therapy, and reliable evaluation of treatment. Our aim is to provide clinicians with intelligent software supporting accurate, efficient and reproducible delineation of the tumor.
Latest News & Blog
We are going to European Oncology and Imaging Convention, 26-27.03.2019 at Birmingham, UK Our Data Scientist Piotr and Michal will speak about "Deep Learning in Oncology" http://www.oncologyconvention.com/speakers/piotr-giedziun--micha322-kraso324/...
We take part in MEDICA - the leading international trade fair for the medical sector in Düsseldorf / Germany from 12 - 15 November 2018. We present our new product for Pathologists and researchers: micro.cancercenter.ai [gallery link="file"...
This year’s TC Disrupt attracted some of the world’s top tech startups with the biggest influencers from Silicon Valley and other parts of the world! Featured guests included Uber CEO Dara Knosrowshahi, Greylock Partners’ Reid Hoffman, Bumble Founder & CEO...
Abstract INTRODUCTION: Automatic functional volume segmentation in PET images is a challenge that has been addressed using a large array of methods. A major limitation for the field has been the lack of a benchmark dataset that would allow direct comparison of the...
We've been on Pioneers in Vienna on 24-25 April 2018.