Abstract: A method for classifying at least one input image representing a target particle in a sample, involves implementing, by data processing of a client, steps of: (b) extracting the characteristic map of the target particle by a convolutional neural network pre-trained on a base of public images; (c) classifying the input image according to the extracted characteristic map.

Abstract: A method for classifying at least one input image representing a target particle in a sample involves implementing, by data processing a client, steps of: (B) extracting a characteristic map of the target particle from the input image; (c) reducing the number of variables in the extracted characteristic map, using the t-SNE algorithm; (d) classifying, unsupervised, the input image based on the characteristic map having a reduced number of variables.

Abstract: A method for classifying at least one input image containing a target particle in a sample, involves implementing, via data-processing of a client, steps of: (b) extracting a vector of characteristics of the target particle, the characteristics being numerical coefficients each associated with one elementary image of a set of elementary images each representing a reference particle, such that a linear combination of the elementary images weighted by the coefficients approximates the representation of the target particle in the input image; (c) classifying the input image depending on the extracted vector of characteristics.

Abstract: A method for classifying a sequence of input images representing a target particle in a sample over time, includes the following steps performed by the data processing of a client, namely: (b) concatenation of the input images in the sequence as a three-dimensional stack; (c) direct classification of the three-dimensional stack using a convolutional neural network, CNN

Abstract: A computer-implemented method for detecting a genome sequence in digital form in a genome of a microorganism in digital form, including:—storing, in a computer memory, a set of digital genome sequences of constant length k, or ‘k-mers’, the set being obtained by sliding, at a constant pitch, a window of length k over the genome sequence;—for each k-mer, determining its absence or presence in the genome;—determining that the genome sequence is present in the genome if the percentage of k-mers detected as present in the genome is higher than a predetermined threshold.