Systems, methods, and apparatuses for implementing stepwise incremental pre-training for integrating discriminative, restorative, and adversarial learning into an ai model

A computer-implemented method, performed by a system having at least a processor and a memory therein to execute instructions for implementing stepwise incremental pre-training for integrating discriminative, restorative, and adversarial learning into a single Artificial Intelligence (AI) model, comprising:

• receiving at the system, a training dataset at the system comprising a plurality of medical images for training a unified AI model;
• executing stepwise incremental pre-training operations to train the unified AI model, comprising:
• pre-training a discriminative encoder via discriminative learning, yielding a pre-trained discriminative encoder;
• attaching the pre-trained discriminative encoder to a restorative decoder to form a skip-connected encoder-decoder;
• pre-training the skip-connected encoder-decoder via joint discriminative and restorative learning, yielding the pre-trained discriminative encoder and pre-trained restorative decoder;
• and;
• associating the pre-trained skip-connected encoder-decoder with an adversarial encoder;
• finalizing training of the AI model by performing discriminative, restorative, and adversarial learning on the training dataset using the unified AI model, yielding the pre-trained discriminative encoder, the pre-trained restorative decoder, and a pre-trained adversarial encoder;
• and
• applying, via the unified AI model, each of discriminative, restorative, and adversarial learning operations through the discriminative encoder, the restorative decoder, and the adversarial encoder, generated via training of the unified AI model, for classifying and annotating medical images.

A non-transitory computer readable storage media having instructions stored thereupon that, when executed by a system having at least a processor and a memory therein, cause the processor to perform the following operations:

• receiving at the system a training dataset comprising a plurality of medical images for training a unified Artificial Intelligence (AI) model;
• executing stepwise incremental pre-training operations to train the unified AI model, comprising:
• pre-training a discriminative encoder via discriminative learning, yielding a pre-trained discriminative encoder;
• attaching the pre-trained discriminative encoder to a restorative decoder to form a skip-connected encoder-decoder;
• pre-training the skip-connected encoder-decoder via joint discriminative and restorative learning, yielding the pre-trained discriminative encoder and a pre-trained restorative decoder;
• and;
• associating the pre-trained skip-connected encoder-decoder with an adversarial encoder;
• and
• finalizing training of the AI model by performing discriminative, restorative, and adversarial learning on the training dataset using the unified AI model, yielding the pre-trained discriminative encoder, the pre-trained restorative decoder, and a pre-trained adversarial encoder;
• and
• applying, via the unified AI model, each of discriminative, restorative, and adversarial learning operations through the discriminative encoder, the restorative decoder, and the adversarial encoder, generated via training of the unified AI model, for classifying and annotating medical images.