Abstract: This disclosure relates to the combining and interaction of multiple artificial intelligence (AI) models for medical image analysis. An example method includes obtaining AI models from model providers and organizing them to form associations. In response to a user request, base models are selected and provided. Additional models are further selected to combine with the base models, and medical image analysis results are presented based on applying a combination of the models to target medical image data.

Abstract: The present invention relates to a method for nonlinear classification of high dimensional data by means of boosting, whereby a target class with significant intra-class variation is classified against a large background class, where the boosting algorithm produces a strong classifier, the strong classifier being a linear combination of weak classifiers. The present invention specifically teaches that weak classifiers classifiers h1, h2, that individually more often than not generate a positive on instances within the target class and a negative on instances outside of the target class, but that never generate a positive simultaneously on one and the same target instance, are categorized as a group of anti-correlated classifiers, and that the occurrence of anti-correlated classifiers from the same group will generate a negative.

Abstract: The present invention relates to a method for nonlinear classification of high dimensional data by means of boosting, whereby a target class with significant intra-class variation is classified against a large background class, where the boosting algorithm produces a strong classifier, the strong classifier being a linear combination of weak classifiers. The present invention specifically teaches that weak classifiers classifiers h1, h2, that individually more often than not generate a positive on instances within the target class and a negative on instances outside of the target class, but that never generate a positive simultaneously on one and the same target instance, are categorized as a group of anti-correlated classifiers, and that the occurrence of anti-correlated classifiers from the same group will generate a negative.