Re-training a model for abnormality detection in medical scans based on a re-contrasted training set

A contrast parameter learning system, comprising:

• at least one processor;
• and
• a memory that stores operational instructions that, when executed by the at least one processor, cause the contrast parameter learning system to:
• generate first contrast significance data for a first computer vision model, wherein the first computer vision model was generated from a first training set of medical scans;
• identify first significant contrast parameters based on the first contrast significance data;
• generate a first re-contrasted training set based on performing a first intensity transformation function on the first training set of medical scans, wherein the first intensity transformation function utilizes the first significant contrast parameters;
• generate a first re-trained model from the first re-contrasted training set, which is associated with corresponding output labels based on abnormality data for the first training set of medical scans;
• generate re-contrasted image data of a new medical scan based on performing the first intensity transformation function;
• generate inference data indicating at least one abnormality detected in the new medical scan based on utilizing the first re-trained model on the re-contrasted image data;
• and
• transmit the inference data for display.

A method comprising:

• generating first contrast significance data for a first computer vision model, wherein the first computer vision model was generated from a first training set of medical scans;
• identifying first significant contrast parameters based on the first contrast significance data;
• generating a first re-contrasted training set based on performing a first intensity transformation function on the first training set of medical scans, wherein the first intensity transformation function utilizes the first significant contrast parameters;
• generating a first re-trained model from the first re-contrasted training set, which is associated with corresponding output labels based on abnormality data for the first training set of medical scans;
• generating re-contrasted image data of a new medical scan based on performing the first intensity transformation function;
• generating inference data indicating at least one abnormality detected in the new medical scan based on utilizing the first re-trained model on the re-contrasted image data;
• and
• transmitting the inference data for storage via a database storage system.