Deep learning-based APT imaging using synthetically generated training data

Machine learning is increasingly applied to address challenges in specifically quantifying APT effect. The models are usually trained on measured data, which, however are usually lack of ground truth and sufficient training data. Synthetically generated data from both measurements and simulations can create training data which mimic tissues better than full simulations, cover all possible variations in sample parameters, and provide the ground truth. We evaluated the feasibility to use synthetic data to train models for predicting APT effect. Results show that the machine learning predicted APT is more close to the ground truth than the conventional multiple-pool Lorentzian fit.