Camera Systems for Deep Learning
Deep learning algorithms already support medical imaging in many areas today and include all methods, such as X-ray, ultrasound, CT and MRI examinations. The increasing automation of diagnostic procedures offers advantages for patients and physicians alike: higher accuracy and reliability of examination results, even more precise diagnostic statements and improved, individually tailored therapeutic measures. The basic prerequisite for the use of artificial neural networks is in many cases machine vision.
In our White Paper, Basler Product Manager Peter Behringer, explains the four steps of a typical image processing process and describes the three types of vision systems for deep learning: embedded systems, PC-based systems, and FPGA frame grabber-based systems.
Furthermore, the White Paper provides valuable insights on
- Deep Learning in Microscopy
- Digital Pathology
- Virtual Staining in Microscopy and on
- How to make Artificial Neural Network-based products commercially viable.