Arizona State University

Shivam Bajpai

Jianming Liang

Described herein are means for generating pre-trained models for nnU-Net through the use of improved transfer learning techniques, in which the pre-trained models are then utilized for the processing of medical imaging. According to a particular embodiment, there is a system specially configured for segmenting medical images, in which such a system includes: a memory to store instructions; a processor to execute the instructions stored in the memory; wherein the system is specially configured to: execute instructions via the processor for executing a pre-trained model from Models Genesis within a nnU-Net framework; execute instructions via the processor for learning generic anatomical patterns within the executing Models Genesis through self-supervised learning; execute instructions via the processor for transforming an original image using distortion and cutout-based methods; execute instructions via the processor for learning the reconstruction of the original image from the transformed image using an encoder-decoder architecture of the nnU-Net framework to identify the generic anatomical representation from the transformed image by recovering the original image; and wherein architecture determined by the nnU-Net framework is utilized with Models Genesis and is trained to minimize the L2 distance between the prediction and ground truth. Other related embodiments are disclosed.

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Tempe, AZ, United States of America

Shivam Bajpai