Automatic image analysis method for automatically recognizing at least one rare characteristic

An automatic image analysis method, said method comprising a learning phase and an analysis phase, said method comprising:

• during said learning phase:
• a step of supervised deep learning of at least one convolutional neural network on the basis of a collection of images associated with annotations for generating at least one trained convolutional neural network, arranged in layers and forming a learning model configured to recognize at least one frequent characteristic in an image to be analyzed, said collection of images comprising images having said at least one frequent characteristic and images having at least one rare characteristic, a frequent characteristic corresponding to a characteristic presenting an appearance frequency on the collection of images which is equal or greater than a frequency threshold;
• a rare characteristic corresponding to a characteristic presenting an appearance frequency on the collection of images which is lower than a frequency threshold;
• an annotation indicating whether a rare or frequent characteristic is present in an image of the collection of images;
• a step of constructing a parameter space of dimension n, with n ≥2, each parameter originating from at least one intermediate layer of said at least one trained convolutional neural network and each parameter representing a dimension of said parameter space;
• a step of determining a presence probability function in said parameter space for at least one rare characteristic on the basis of a projection, in said parameter space, of said images of the collection of images and annotations relating to said at least one rare characteristic;
• during said analysis phase:
• for an image to be analyzed, a step of recognizing said at least one rare characteristic in said image to be analyzed on the basis of the presence probability function determined for said at least one rare characteristic.