Just like doctors, algorithms also have to first learn how to draw the right conclusions. However, there are two distinct differences: First, algorithms excel where physicians tend to tire quickly. For instance, when they have to quickly scan many images and search for potential findings. Secondly, algorithms do not require years of study to complete these types of tasks. This eases physician workload and frees up their time for other responsibilities that necessitate specialist in-depth knowledge. "You do not search for methods to replicate and artificially create intelligence. Instead, you look for ways to teach computers to solve a cognitive task as quickly and efficiently as possible and do at least as well as a human being," states Dr. Markus Wenzel from the Fraunhofer Institute for Medical Image Computing MEVIS in an interview with MEDICA-tradefair.com.
Humans assign algorithms a cognitive task, that is, a task that relates to perception. Human beings are very good at this because our sense of sight and our sense of hearing are made to recognize faces, voices and language even if those are not clearly visible, as is the case in changing light conditions or ambient noise settings for instance. These tasks are extremely difficult for computers because it is impossible or difficult to describe and explain them via logical rules, which is what computers are based on.
In radiology, the type of task might be, "Find all MRI scans showing a tumor". The researcher feeds the algorithm image data for learning purposes. Simply put, the algorithm then compares the image data, identifies the features detectable on these images and recognizes patterns and regularities. Based on this information, the algorithm can perform and solve the task and independently detect images with tumors. When it comes to the algorithm, more data gives better results. "A computer has more opportunities when it can choose the important features by itself. This is why deep learning is often also called feature learning because the computer picks the features it deems important in the images to solve the task," says Wenzel.