Better/Faster Cancer Diagnosis
= Medical Images + Deep Learning
The important thing is not to stop questioning. Curiosity has its own reason for existing. One cannot help but be in awe when he contemplates the mysteries of eternity, of life, of the marvelous structure of reality. It is enough if one tries merely to comprehend a little of this mystery every day. Never lose a holy curiosity.
State of the art technology gives us the chance to improve human health conditions substantially. With the use of various imaging techniques, human body and tissues can be examined in great detail which in turn makes it possible to analyze internal phenomena. Well educated and experienced doctors can give a diagnosis at the most specific and detailed level. There is, however, one fundamental problem – time. Fortunately, there are also well educated and experienced people who can make their work easier, faster and much more efficient.
By connecting the new technology of medical imaging and the most advanced and futuristic solutions from the field of mathematics and computer science (machine and deep learning) we can provide doctors with all the necessary tools to work with medical images. Cancer Center applies deep learning techniques to the field of oncology/radiology. It has amassed a huge training set of medical images along with categorization technology that will allow computers to predict multiple diseases with better-than-human accuracy. In 2016, the company unveiled new software algorithm to help recognize/diagnose lung cancer from PET images.
Don’t waste your time! Let us do the work! We can segment images, find regions of interest, provide statistical descriptions of images, count cells, mitosis or even recognize their kind.
Don’t hesitate, just save lives.
- Automated Medical Image Analysis
- MRI (Magnetic Resonance Imaging)
- PET / CT
- Histopathology (Whole Slide Imaging)
- General planimetry
- Machine Learning / Deep Learning
MRI, DICOM Analysis
It is one of the most important and most profitable diagnostic tools. Not only this medical imaging modality is not invasive but also can be applied in many different scenarios, obtaining images of pretty much every part of the human body. In case of the prostate cancer, our software/deep learning algorithms can be used to find Region of Interest (ROI), cancer segmentation automatically.
In terms of precision, there are no better methods of seeing a single tissue as… to examine the tissue under the microscope. Every histopathologist would agree that sometimes finding the most descriptive part of the slide is a harder task than it is to make the diagnosis itself. Fortunately it is not a problem for us. We can localize the most important parts of the slide, what is more we can extract the very factors that influence diagnostic models the most.
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.