A group of researchers developed an artificial intelligence capable of generating sets of images of an MRI of a human brain. The technology is designed to increase the effectiveness of training AI, specializing in the diagnosis of brain cancer. Tests showed that the effectiveness of diagnostic programs trained on generated kits increased by 14%.
The project was implemented jointly by specialists from NVIDIA, the Mayo Clinic and the Clinical Data Research Center. Development based on the generative and adversarial network structure (GAN) was conducted on the NVIDIA DGX platform using the PyTorch deep training systems. Two interconnected artificial intellects were used. One network generated its own MRI snapshots on the basis of real ones, and the second tried to distinguish real from fake ones.
GAN automatically marks the created sets of MRI images, which significantly speeds up learning. With manual annotation, this work takes experts many hours. In addition, since the system does not consider the brain and tumor as a whole, the operator can correct the picture by moving the tumor or changing its size.
Hu Chang, one of the authors of the study, said that the generated MRI kits also solve the problem of using confidential information. These pictures form a medical secret, and permission is required to use them. And the resulting system can be publicly available.
Hardware limitations forced the team to reduce the resolution of the original images by 8 times. Also, at the moment, neoplasms sometimes look "superimposed" on a snapshot. In the future, researchers plan to eliminate these shortcomings.
When teaching neural networks-diagnosticians, the question of the availability of training datasets is relevant. Developed by German scientists, AI, which determines myocardial infarction by ECG, used as input only 200 records. According to the creators, this seriously worsened the efficiency of the system. Tools that create datasets for learning neural networks are designed to help solve this problem.