Medical image processing is one the hottest AI techniques in the healthcare domain. The underlying idea is to use the advance computer vision, statistical and machine learning algorithms to extract information from various types of medical images (X-rays, CT, MRT, etc.) and present it a human-readable format for further estimation by medical professionals.
We were commissioned to develop an algorithm for medical image segmentation to enable an automatic phenotypes detection.
We have successfully developed models for various image processing tasks (segmentation, detection, classification, etc.). All models were wrapped with a user-friendly API. The customer was also provided with a clear code documentation and a report with the experiment details and the results analysis.
To fulfill the task, we have used the advanced computer vision and machine learning algorithms for image processing and recognition including rescaling, normalization, cascade filtering, convolutional neural networks, deep learning, etc. Highly trained medical professionals have analyzed and validated the algorithms results. Additionally, we have received a feedback on the nature and the importance of errors, which might also improve the system performance from medical and business perspective.
Probabilistic graphical models
unsupervised sound segmentation
Hi, we are Sciforce - a company where the integration of various branches of science builds up a powerful force to create robust software solutions. Working at the intersection of Computer Science with other technical, natural and humanitarian sciences let us go beyond traditional IT services and become both technical and scientific forces to our customers.