Dr. Alexander Krannich on how TCC is using Generative Adversarial Networks to improve individualised therapy and patient care in the future.
Generative Adversarial Networks (GANs) are a fascinating advancement in AI, driving breakthroughs in data generation. Here’s a quick look at how they work: 1. Two parts: Generator and Discriminator GANs consist of two neural networks: the Generator, which creates new data, and the Discriminator, which evaluates whether that data is real or fake. 2. They challenge each other The Generator and Discriminator are in constant competition. The Generator's goal is to create data so realistic that it fools the Discriminator, while the Discriminator’s job is to become better at detecting what's real and what’s generated. 3. The Generator makes new data Starting from random noise, the Generator learns to create data that mimics real-world samples. This can include images, audio, text, or other types of data. 4. The Discriminator checks if it’s real The Discriminator acts as a gatekeeper, constantly improving its ability to identify real data from fake and giving feedback to the Generator on how to improve. 5. They keep getting better! As training continues, both the Generator and Discriminator improve over time. The Generator produces more realistic data, and the Discriminator gets better at spotting fake data. The result? Highly convincing, synthetic data that can be used for a variety of applications, from image generation to data augmentation. TCC GmbH tested Generative Adversarial Networks (GAN) to investigate similarities in patient patterns. This can help to tailor therapy decisions in the future and to improve patients’ life. #machinelearning #datascience #GAN #AI #TCCanalytics #digitalcare
