Abstract: A breast ultrasound diagnosis method using weakly supervised deep-learning artificial intelligence comprises: an ultrasound image preprocessing step of generating input data including only an image region necessary for learning, by deleting personal information about a patient from a breast ultrasound image; a deep-learning step of receiving the input data, obtaining a feature map from the received input data by using a convolutional neural network (CNN) and global average pooling (GAP), and carrying out re-learning; and a differential diagnosis step of determining the input data as one of normal, benign, and malignant by using the GAP, and when the input data is determined to be malignant, calculating a probability of malignancy (POM) indicating accuracy of the determination.

Abstract: Disclosed is a method for quantifying a breast composition performed by a computing device according to an exemplary embodiment. The method may further include: acquiring an ultrasound image; identifying a breast region in the ultrasound image; identifying a fibro-glandular tissue region in the ultrasound image; and quantifying at least one of a breast tissue component or a glandular tissue component based on the identified breast region and the identified fibro-glandular tissue region.