Ovarian toxicity assessment in histopathological images using deep learning

A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions including:

• obtaining a set of images of tissue slices from one or more ovaries treated with an amount of a compound;
• inputting the set of images into a neural network model constructed as a one-stage detector using focal loss as at least a portion of the loss function;
• predicting, using the neural network model, coordinates for a bounding box around one or more objects within the set of images that are identified as corpora lutea (CL);
• outputting, using the neural network model, the set of images with the bounding box around the one or more objects that are identified as the CL based on the coordinates predicted for the bounding box;
• counting the bounding boxes within the set of images output from the deep learning neural network to obtain a CL count for the ovary;
• and
• determining an ovarian toxicity of the compound at the amount based on the CL count for the ovary.

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:
• obtaining a set of images of tissue slices from one or more ovaries treated with an amount of a compound;
• inputting the set of images into a neural network model constructed as a one-stage detector using focal loss as at least a portion of the loss function;
• predicting, using the neural network model, coordinates for a bounding box around one or more objects within the set of images that are identified as corpora lutea (CL) based on the classifying of the objects;
• outputting, using the neural network model, the set of images with the bounding box around the one or more objects that are identified as the CL based on the coordinates predicted for the bounding box;
• counting the bounding boxes within the set of images output from the deep learning neural network to obtain a CL count for the ovary;
• and
• determining an ovarian toxicity of the compound at the amount based on the CL count for the ovary.