Machine learning from noisy labels for abnormality assessment in medical imaging

A method for machine learning abnormality assessment in medical imaging by a machine, the method comprising:

• obtaining training data comprising medical images and ground truth labels for the medical images, the ground truth labels designating an abnormality represented by the medical images;
• machine training, by the machine, a model from the training data, the machine training using a loss function, the loss function including a regularization, the regularization comprising a noise regularization, where the noise regularization includes a predetermined function of prior label noise probabilities;
• and
• storing the model resulting from the machine training in a memory.