Our deep-learning model for brain tumor segmentation is top ranked in BraTS and FeTS 2022 competing against world-class specialists in AI.
In the area of medical imaging the semi-automatic segmentation term can be encountered. We provide a short explanation & examples of its use.
Learn how we worked on the automated connection of the inflows and outflows of a segmented 3D model of brain aneurysm to points in a 3D space.
We use ITK-SNAP in our projects. Our extension allows for defining and setting predefined contrast windows for specific tissue types.
We introduce basic radiomics pipelines and dive into the current challenges and opportunities of this promising field of research.
Imaging biomarkers provide information on tumor prognosis & characteristics promising better a better future of cancer patients.
AI algorithms in drug development allow us for quicker patient response assessment and earlier go/no-go decisions.
AI-enabled analysis of medical images becomes more and more needed in clinical trials and the pharmaceutical industry.
One of the most essential processes of medical imaging is image segmentation. Quality of our resulting AI technologies starts with Med-Team.
We have organized an Ask Us Anything session (#ama) with our BraTS challenge team – the answer all your questions about deep learning models.
At Graylight Imaging we are dedicated to lowering the barriers that limit girls and #WomenInScience.
How our experience in AI-enabled MRI analysis of LGG/HGG has translated into an algorithm to segment pediatric optic pathway gliomas from MRI?
Graylight Imaging Data Scientists top ranked in RSNA-ASNR-MICCAI 2021 Brain Tumor Segmentation (BraTS) Challenge
Graylight Imaging team took sixth place at this prestigious challenge – Brain Tumor Segmentation (BraTS) Challenge.
Explore the new brand of Future Processing Healthcare – Graylight Imaging and discover the many ways you can innovate with us.
Artificial Radiologist – the difference between machine learning and artificial intelligence in healthcare
Is artificial intelligence in healthcare a mindless pattern comparator, that does not understand the essence of the disease it is diagnosing?
AI-powered medical image analysis can not only build automated pipelines for the most tedious tasks but can make us see beyond the visible.
The path we have gone through in the process of medical image segmentation allows our AI-enabled segmentations to meet high standards.
3D medical image processing cardiovascular models prepared via the segmentation process have very wide applications.
We will look at the medical image segmentation process and our methodology and give some tips on how to improve this process.
Digital medical image segmentation and analysis is a natural consequence of advances in medical imaging technology.
Well prepared data is the basis in medical machine learning project. We present our own process of ground-truth preparation.
Lung segmentation algorithms are based on conventional image processing or ML or a combination of both. Read about the first method.
The key to risk management in medical devices is the safety of patients and users of our products. Is it any different for software?
2020 was full of world-shifting events, but also in our team – AI healthcare company – some major things happened.
We would like to discuss the challenges user experience design in medical software faces while designing for health information technology
Grant partner – how to determine the attributes that would characterize our potential partner or subcontractor? We give you some hints.
Impressions on applications of AI in medicine from Intelligent Health AI 2020 by our Machine Learning experts.
Artificial intelligence in radiology is the proper weapon that we need to empower radiologists with. Read our highlights from ECR 2020!
Our insights into the pandemic experiences we all share, which in our opinion have a chance to contribute a lot of good.
Is managing medical software development different from managing other projects? Our Team Leader talks openly about practice.
Quality assurance in a medical project is an area where it is important to understand the nature of the industry and its needs
How to address concerns about the organizational culture of a software outsourcing partner and its impact on the success of a project?
The number of projects carried out by remote teams or hybrid teams grows every year. We share our experience in managing such dispersed teams.
A couple of reflections on requirements involved when it comes to software as a medical device. Is ISO 13485 software provider crucial?
How to establish an objective measurement, crucial for clinical assessment, to gauge the quality of our results in a medical r&d project?
How do you make sure that the priorities are well understood? Read abour the roles in IT projects.
Recruitment process in IT – by being close to this market, we see some constants and share our perspective.
Read about the proper sequence of actions and help yourself with a few proven tools.
Krzysztof Kotowski, our AI Specialist, tackles techniques of algorithms-training.
I would like to raise the issue of the analysis of the medical product idea, which is an attempt to assess if it has a chance of success.
Is it possible to apply the MVP idea where a proper development process and certification is a must?
I share my observations regarding the development of medical software from the application service provider point of view.
How about simplifying the “application of artificial intelligence in the medical sector” issue by going BACK TO BASICS?
To deliver a solution compliant with medical standards be aware that ISO 13485 certification is not a simple list of instructions.
A comparison of both ways in terms of such areas as culture, talent pool, confidentiality, communication, control, risk and projects’ price.
Although r&d outsourcing has its cost-related history, the trends begin to change as companies strive to gain a competitive advantage.