Image diagnosis support apparatus, image diagnosis support program, and medical image acquisition apparatus including the same

A non-transitory computer-readable medium storing an image diagnosis support program causing a computer to execute steps comprising:

• training a machine learning algorithm using a plurality of training images to detect a tissue;
• calculating an image reliability for each of a plurality of medical images;
• calculating a degree of contribution of each of the plurality of medical images to a diagnostic target based on a sum or a product of the image reliability and a degree of appropriateness of each medical image for a diagnostic target tissue, the degree of appropriateness being a predetermined value stored in a table, the degree of appropriateness selected from the table based on a type of each medical image and the diagnostic target tissue;
• adjust a contrast of one or more of the plurality of medical images to be a same contrast of the training images using one or more conversion functions,
• generate an image for detection by subjecting the adjusted images to weighted addition based on the calculated degree of contribution of each of the plurality of medical images,
• input the generated image for detection to the trained machine learning algorithm to detect a presence or an absence of the diagnostic target tissue in the generated image for detection, detection result, which indicates the presence or absence of the diagnostic target tissue.

A medical image acquisition apparatus, comprising:

• a memory coupled a processor, the memory storing instructions that when executed configure the processor to:
• train a machine learning algorithm using a plurality of training images to detect a tissue,
• evaluate an image quality and calculates an image reliability for each of a plurality of medical images,
• calculate a degree of contribution of each of the plurality of medical images to a diagnostic target based on a sum or a product of the image reliability and a degree of appropriateness of each medical image for a diagnostic target tissue, the degree of appropriateness being a predetermined value stored in a table, the degree of appropriateness selected from the table based on a type of each medical image and the diagnostic target tissue,
• adjust a contrast of one or more of the plurality of medical images to be a same contrast of training images using one or more conversion functions,
• generate an image for detection by subjecting the adjusted images to weighted addition based on the calculated degree of contribution of each of the plurality of medical images, and
• input the generated image for detection to the trained machine learning algorithm to detect a presence or an absence of the diagnostic target tissue in the generated image for detection, as a detection result, which indicates the presence or absence of the diagnostic target tissue.