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 based on a collection of images associated with annotations for generating at least one trained convolutional neural network, arranged in layers and forming a learning model able to recognise at least one frequent feature in an image to be analysed; said collection of images comprising images having said at least one frequent feature and images having at least one rare feature; a frequent feature corresponding to a feature whose frequency in the considered collection of images is greater than or equal to a threshold frequency; a rare feature corresponding to a feature whose frequency in the considered collection of images is less than the threshold frequency; an annotation indicating whether the at least one rare or frequent feature is present or absent in each image of the collection of images; - a step of constructing an n-dimension parameter space, 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 so-called rare feature based on a projection, in said parameter space, of said images of the collection of images having annotations relating to said at least one so-called rare feature; • during said analysis phase, - for an image to be analysed, a step of recognising said at least one so-called rare feature in said image to be analysed based on the presence probability function determined for said at least one so-called rare feature. • during said learning phase, - a step of supervised deep learning of at least one convolutional neural network based on a collection of images associated with annotations for generating at least one trained convolutional neural network, arranged in layers and forming a learning model able to recognise at least one frequent feature in an image to be analysed; said collection of images comprising images having said at least one frequent feature and images having at least one rare feature; a frequent feature corresponding to a feature whose frequency in the considered collection of images is greater than or equal to a threshold frequency; a rare feature corresponding to a feature whose frequency in the considered collection of images is less than the threshold frequency; an annotation indicating whether the at least one rare or frequent feature is present or absent in each image of the collection of images; - a step of constructing an n-dimension parameter space, 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 so-called rare feature based on a projection, in said parameter space, of said images of the collection of images having annotations relating to said at least one so-called rare feature; - a step of supervised deep learning of at least one convolutional neural network based on a collection of images associated with annotations for generating at least one trained convolutional neural network, arranged in layers and forming a learning model able to recognise at least one frequent feature in an image to be analysed; said collection of images comprising images having said at least one frequent feature and images having at least one rare feature; a frequent feature corresponding to a feature whose frequency in the considered collection of images is greater than or equal to a threshold frequency; a rare feature corresponding to a feature whose frequency in the considered collection of images is less than the threshold frequency; an annotation indicating whether the at least one rare or frequent feature is present or absent in each image of the collection of images; - a step of constructing an n-dimension parameter space, 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 so-called rare feature based on a projection, in said parameter space, of said images of the collection of images having annotations relating to said at least one so-called rare feature; • during said analysis phase, - for an image to be analysed, a step of recognising said at least one so-called rare feature in said image to be analysed based on the presence probability function determined for said at least one so-called rare feature. - for an image to be analysed, a step of recognising said at least one so-called rare feature in said image to be analysed based on the presence probability function determined for said at least one so-called rare feature.