Systems, methods, and apparatuses for implementing discriminative, restorative, and adversarial (dira) learning using stepwise incremental pre-training for medical image analysis

Abstract

The system receives a plurality of medical images and integrates Self-Supervised machine Learning (SSL) instructions for performing a discriminative learning operation, a restorative learning operation, and an adversarial learning operation into a model for processing the received plurality of medical images. The model is configured with each of a discriminative encoder, a restorative decoder, and an adversarial encoder. Each of the discriminative encoder and the restorative decoder are configured to be skip connected, forming an encoder-decoder. Step-wise incremental training to incrementally train each of the discriminative encoder, the restorative decoder, and the adversarial encoder is performed, in particular: pre-training the discriminative encoder via discriminative learning; attaching the pre-trained discriminative encoder to the restorative decoder to configure the encoder-decoder as a pre-trained encoder-decoder; and training the pre-trained encoder-decoder of the model using joint discriminative and restorative learning. The pre-trained encoder-decoder is associated with the adversarial encoder. The pre-trained encoder-decoder associated with the adversarial encoder is trained through discriminative, restorative, and adversarial learning to render a trained model for the processing of the received plurality of medical images. The plurality of medical images are processed through the model using the trained model.