Machine learning system for diagnosing breast lesions, adaptable to the clinician and based on assertiveness

Composite machine learning system for diagnosing breast lesions that is adaptable to the clinician and based on assertiveness and using one or more computer processors characterised by including at least one imaging device and machine-learning system for diagnosing breast lesions,

• the machine-learning system for diagnosing breast lesions being i) adaptable to the clinician and based on assertiveness, ii) using one or more computer processors, and iii) comprising:
• a first operable database to maintain a plurality of characteristics obtained from digital images of breast sections comprising annotated patterns, at least part of the annotated patterns being associated with breast lesions;
• a second operable database to maintain a plurality of demographic characteristics of clinicians;
• at least one server interconnected with the first database and the second database, the at least one server being configured for:
• training one or more classifier computer agents on the basis of the annotated patterns in the respective first database;
• computationally obtaining one or more digital images of a breast section;
• obtain one or more demographic characteristics of a clinician from a computational interaction provided by the clinician and the plurality of demographic characteristics of clinicians recorded in the respective second database;
• use one or more classifier agents to computationally determine a classification and respective segmentation associated with one or more breast lesions by determining one or more probable physical parameters, the probable physical parameters comprising potentially existing physical parameters for which a probability of occurrence is determined;
• in which the computational determination of probable physical parameters is adapted based on the demographic characteristics of the clinician.