Detection and classification using a single machine learning model

A computer implemented method of detection and multi-class classification of objects of interest in an image, using a single machine learning model, the method comprising:

• applying a machine learning model on at least one input image, wherein the machine learning model is a model, which has been trained using a partially and weakly labelled training dataset of images;
• for each input image, the machine learning (ML) model is configured to provide as output, C probability maps, where C is defined according to the number of classes in a group of classes, which includes at least two object-related classes and a background class;
• and each map of the C probability maps corresponds to a respective class and wherein the C probability maps provide collectively, for each pixel in the input image, a respective probability vector comprising C probability values, each value indicating the probability that the pixel belongs to a certain class in the group of classes;
• applying post-processing on the output, comprising:
• detecting one or more objects of interest in the at least one input image, based on the probability vectors;
• and
• classifying each detected object of interest to a respective class in the group of classes, based on the probability values, in the probability maps, of pixels located in the detected object of interest.

A computer program product operable in a computer and comprising instructions stored on a non-transitory computer-readable medium for causing the computer to execute a method of detection and multi-class classification of one or more objects in an image, using a single machine learning model for both detection and classification, wherein the product is produced by the processes of:

• obtaining a training dataset comprising a collection of partially and weakly labelled images, where in each image only part of the one or more objects are labeled, and only part of the pixels of each labeled object are labeled;
• using the training dataset for training the machine learning model comprising:
• generating for each partially and weakly labeled image C probability maps, wherein each map corresponds to a respective class and comprises, with respect to each labelled pixel a respective probability value indicative of a probability that the pixel belongs to the respective class;
• wherein the C probability maps provide collectively, for each labelled pixel, a respective probability vector comprising C probability values each value indicating the probability that the pixel belongs to a respective class;
• and
• iteratively applying a loss function on the training dataset.

A computer implemented method of training machine learning model for detection and multi-class classification of one or more objects in one or more images using a single machine learning model for both detection and classification, the method comprising:

• applying an encoding expansion process on the weakly and partially labeled dataset before training, comprising:
• for each labeled object in the training dataset:
• automatically defining a first masked region surrounding at least one manually labeled pixel in the labeled object, wherein all pixels in the first region are classified to the same class as the at least one manually labeled pixel, and a second masked region surrounding the first mased region, wherein the area between the first masked region and the second masked region corresponds to background,
• using the training dataset for training the machine-learning model comprising:
• generating for each partially and weakly labeled image C probability maps, wherein each map corresponds to a respective class and comprises, with respect to each labelled pixel a respective probability value indicative of a probability that the pixel belongs to the respective class;
• wherein the C probability maps provide collectively, for each labelled pixel, a respective probability vector comprising C probability values each value indicating the probability that the pixel belongs to a respective class;
• and
• iteratively applying a loss function on the training dataset.

A diagnostic method for detecting and multi-class classifying of sub-cellular localization of at least one biomarker, in at least one object of at least one biological sample, the method comprising:

• applying a machine learning model on at least one input image of said sample, wherein the machine learning model is a model, which has been trained using a partially and weakly labelled training dataset of images;
• for each input image, the machine learning (ML) model is configured to provide as output, C probability maps, where C is defined according to the number of classes in a group of classes, which includes at least two object-related classes and a background class;
• and each map of the C probability maps corresponds to a respective class, wherein each object-related class in the group of classes reflects a respective distribution of at least one biomarker within the object;
• wherein the C probability maps provide collectively, for each pixel in the input image, a respective probability vector comprising C probability values, each value indicating the probability that the pixel belongs to a certain class in the group of classes;
• applying post-processing on the output, comprising:
• detecting one or more objects of interest in the at least one input image, based on the probability vectors;
• and
• classifying each detected object of interest to a respective class in the group of classes, based on the probability values, in the probability maps, of pixels located in the detected object of interest, thereby identifying one or more classified objects in the at least one input image.