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
Our paper published: Machine learning methods for accurate delineation of tumors in PET images Jakub Czakon, Filip Drapejkowski, Grzegorz Zurek, Piotr Giedziun, Jacek Zebrowski, Witold Dyrka (Submitted on 29 Oct 2016) In oncology, Positron Emission Tomography imaging...
It's good news: Our team won PET segmentation challenge using a data management and processing infrastructure! Automated segmentation of PET images for the delineation of tumor volumes has been the focus of intense research efforts for the last few years. There has...
Our team have released our new software package for tumor delineation in PET images. It can be found at github.com/stermedia/SterSEG. Package has been developed in python.
We are goint to Athens and present our results in challenges: PETSEG: PET Segmentation Challenge Using a Data Management and Processing Infrastructure The PETSEG challenge will provide a comparative study of a range of state-of-the-art algorithms for PET image...