Abstract: Methods, apparatus and systems for robust and accurate detection of anomalies in medical images and electrocardiograms are disclosed. One example system for training a neural network engine includes a processor that is configured to receive a set of training electrocardiogram signals. At least one electrocardiogram signal in the set of training electrocardiogram signals is associated with metadata identifying a region of interest that includes a heart anomaly. The processor is configured to input the set of training electrocardiogram signals into the neural network engine. The neural network engine is trained using an objective function having a first regularization parameter and a second regularization parameter. The processor is also configured to operate the neural network engine to identify the heart anomaly by classifying the set of training electrocardiogram signals and adjust the neural network engine based on the identified heart anomaly and the metadata.