Patent ID: 11875559
Assignee: OBVIO HEALTH USA, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 19:
20. The method of claim 19, wherein the method further comprises further training the second convolutional neural network to classify stool depicted in an image, such further training comprising

(i) generating a seventh plurality of digital images from the fourth plurality of digital images by, for each respective digital image of the third plurality of digital images, applying one or more random transformations to generate one or more augmented images forming part of the seventh plurality of digital images, and
(ii) for each respective batch of digital images of the seventh plurality of digital images, for each of a plurality of iterations,
(A) calculating, by the second convolutional neural network for each respective digital image of the respective batch of digital images, a respective set of class probability values for the respective digital image, each class probability value being calculated based on one or more parameters associated with one or more layers of the second convolutional neural network, the one or more parameters including one or more weight parameters and one or more bias parameters,
(B) calculating, by the second convolutional neural network based on a loss function, a respective loss value for the respective batch, such calculating comprising
(I) calculating, for each respective digital image of the respective batch, a respective loss value based on the calculated respective class probability values and a respective label associated with the respective digital image representing an indication of a classification of stool in the digital image on a rating scale by a human rater, and
(II) determining the respective loss value for the respective batch based on the calculated loss values for the digital images of the respective batch, and

(C) updating one or more parameters of the second convolutional neural network, such updating comprising
(I) calculating a gradient of a matrix of the calculated class probability values,
(II) starting from this calculated gradient of the matrix of the calculated class probability values, backpropagating through layers of the second convolutional neural network and calculating gradients for parameters associated with these layers, including weight parameters and bias parameters associated with these layers, this backpropagation involving at least some use of skip connections, and
(III) performing, for each respective parameter of a set of one or more parameters of the second convolutional neural network, a parameter update based on a corresponding calculated gradient for that respective parameter and a step size value.