System and method for detecting recurrence of a disease

A method for determining a recurrence of a ductal carcinoma in situ (DCIS) in a patient, the method comprising:

• generating a plurality of medical images of breast tissue of the patient;
• determining a plurality of recurrence probabilities from the plurality of medical images, wherein the plurality of recurrence probabilities includes a first recurrence probability, a second recurrence probability and a third recurrence probability, wherein the first recurrence probability is determined from H&E biopsy sample images, wherein determining the first recurrence probability comprises extracting fixed image patches of predefined pixels from the H&E biopsy sample images, wherein determining the first recurrence probability further comprises automatically mapping each of the fixed image patches to an initial latent space, wherein determining the first recurrence probability further comprises refining the initial latent space to generate refined latent space by encoding patches generated by a generative adversarial network (GAN) model that captures features of aggressive DCIS, and wherein determining the first recurrence probability further comprises using a deep learning (DL) network to classify the refined latent space into aggressive patches and non-aggressive patches and to predict the first recurrence probability based on the aggressive patches and non-aggressive patches;
• and
• determining a recurrence of the DCIS based on the plurality of recurrence probabilities and clinicopathological data of the patient using a Bayesian network.