Weakly supervised multi-task learning for cell detection and segmentation

A non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions including:

• accessing, by a data processing system, a plurality of images of one or more cells;
• extracting, by the data processing system, three labels from the plurality of images, wherein the three labels are extracted using a Voronoi transformation, a local clustering, and application of repel code;
• training, by a multi-task scheduler of the data processing system, a convolutional neural network model based on three loss functions corresponding to the three labels, wherein the convolutional network model comprises a plurality of model parameters;
• generating, by the convolutional neural network model, a nuclei probability map and a background probability map for each of the plurality of images based on the training with the three loss functions;
• comparing, by the data processing system, the nuclei probability map and the background probability map to the three labels;
• updating, by the data processing system, the plurality of model parameters based on the comparing the nuclei probability map and the background probability map to the three labels to minimize the three loss functions;
• and
• providing, by the data processing system, a trained convolutional neural network model with the updated plurality of model parameters.

A system comprising:

• one or more data processors;
• and
• a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform actions including:
• accessing, by a data processing system, a plurality of images of one or more cells;
• extracting, by the data processing system, three labels from the plurality of images, wherein the three labels are extracted using a Voronoi transformation, a local clustering, and application of repel code;
• training, by a multi-task scheduler of the data processing system, a convolutional neural network model based on three loss functions corresponding to the three labels, wherein the convolutional network model comprises a plurality of model parameters;
• generating, by the convolutional neural network model, a nuclei probability map and a background probability map for each of the plurality of images based on the training with the three loss functions;
• comparing, by the data processing system, the nuclei probability map and the background probability map to the three labels;
• updating, by the data processing system, the plurality of model parameters based on the comparing the nuclei probability map and the background probability map to the three labels to minimize the three loss functions;
• and
• providing, by the data processing system, a trained convolutional neural network model with the updated plurality of model parameters.