Today, machine learning plays a key role in many health-related realms, including the development of new medical procedures, the handling of patient data and records and the treatment of chronic diseases. Many experts said that machine learning has virtually endless applications in the healthcare industry. The healthcare industry has advanced dramatically, along with the emergence of machine learning. Then, How machine learning is leveraged in healthcare?
InnerEye is a research project that uses state of the art machine learning technology to build innovative tools for the automatic, quantitative analysis of three-dimensional radiological images. Project InnerEye turns radiological images into measuring devices.
Microsoft's Project InnerEye employs machine learning to differentiate between tumors and healthy anatomy using 3D radiological images that assist medical experts in radiotherapy and surgical planning, among other things. InnerEye is used in the United Kingdom to produce 3D imaging that pinpoints the precise location of tumors and enables more accurately targeted radiotherapy. Project InnerEye develops machine learning techniques for the automatic delineation of tumors as well as healthy anatomy in 3D radiological images.
The InnerEye technology may enable:
Project InnerEye builds upon many years of research in computer vision and machine learning. It employs algorithms such as Deep Decision Forests (as used already in Kinect and Hololens) as well as Convolutional Neural Networks (as available in CNTK) for the automatic, voxel-wise segmentation of medical images. The technology is being designed with the guidance of expert medical practitioners. The results of our machine learning operations can be readily refined and adjusted by expert clinical researchers until they are completely satisfied with the results. They maintain full control of the results at all times.