Abstract: A method for validating a trained artificial intelligence (AI) model on a device is provided. The method includes deploying a validation model generated by applying a plurality of anticipated configurational changes associated with the trained AI model requiring validation. Further, the method includes providing input data to each of the validation model and the trained AI model for receiving an output from each of the validation model and the trained AI model, wherein the output of the validation model is further based on one or more actual configurational deviations that occurred during training of the trained AI model since deployment of the trained AI model on the device. Furthermore, the method includes combining the output of each of the validation model and the trained AI model to validate the trained AI model.

Abstract: Provided are systems and methods for optimizing neural networks for on-device deployment in an electronic device. A method for optimizing neural networks for on-device deployment in an electronic device includes receiving a plurality of neural network (NN) models, fusing at least two NN models from the plurality of NN models based on at least one layer of each of the at least two NN models, to generate a fused NN model, identifying at least one redundant layer from the fused NN model, and removing the at least one redundant layer to generate an optimized NN model.

Abstract: Techniques for training a first electronic neural network classifier to identify a presence of a particular property in a novel supra-image while ignoring a spurious correlation of the presence of the particular property with a presence of an extraneous property are presented.

Abstract: Techniques for determining a presence of a pathology property in a supra-image are presented. The techniques can include receiving an electronic evaluation supra-image; providing the electronic evaluation supra-image to an electronic neural network that has been trained, using a training corpus of training supra-images and on an electronic computer, to determine the presence of the pathology property in a supra-image, each training supra-image including at least one image, each image corresponding to a plurality of components, wherein each training supra-image of the training corpus is associated with a respective electronic label indicating whether the pathology property is present, where the training corpus is sufficient to train the electronic neural network to determine a presence of the pathology property; receiving from the electronic neural network an output indicative of whether the pathology property is present in the evaluation supra-image; and providing the output.

Abstract: A method for processing an input frame for an on-device AI model is provided. The method may include obtaining an input frame. The method may include building at least one kernel independent of the scale of the input frame by passing input variables to the at least one kernel using preprocessor directives independent of the scale of the input frame. The method may include inputting the input frame to the on-device AI model including the at least one kernel independent of the scale of the input frame. The method may include processing the input frame in the on-device AI model.

Abstract: Techniques for training a first electronic neural network classifier to identify a presence of a particular property in a novel supra-image while ignoring a spurious correlation of the presence of the particular property with a presence of an extraneous property are presented.

Abstract: A method for generating an optimal neural network (NN) model may include determining intermediate outputs of the NN model by passing an input dataset through each intermediate exit gate of the plurality of intermediate exit gates, determining an accuracy score for each intermediate exit gate of the plurality of intermediate exit gates based on a comparison of the final output of the NN model with the intermediate output, identifying an earliest intermediate exit gate that produces the intermediate output closer to the final output based on the accuracy score, and generating the optimal NN model by removing remaining layers of the plurality of layers and remaining intermediate exit gates of the plurality of intermediate exit gates located after the determined earliest intermediate exit gate.

Abstract: Techniques for stain normalization image processing for digitized biological tissue images are presented. The techniques include obtaining a digitized biological tissue image; applying to at least a portion of the digitized biological tissue image an at least partially computer implemented convolutional neural network trained using a training corpus including a plurality of pairs of images, where each pair of images of the plurality of pairs of images includes a first image restricted to a lightness axis of a color space and a second image restricted to at least one of: a first color axis of the color space and a second color axis of the color space, such that the applying causes an output image to be produced; and providing the output image.

Abstract: Computer-implemented techniques for classifying a tissue specimen are presented. The techniques include obtaining an image of the tissue specimen; segmenting the image into a first plurality of segments; selecting a second plurality of segments that include at least one region of interest; applying an electronic convolutional neural network trained by a training corpus including a set of pluralities of tissue sample image segments, each of the pluralities of tissue sample image segments labeled according to one of a plurality of primary pathology classes, where the plurality of primary pathology classes consist of a plurality of majority primary pathology classes, where the plurality of majority primary pathology classes collectively include a majority of pathologies according to prevalence, and a class for tissue sample image segments not in the plurality of majority primary pathology classes, such that a primary pathology classification is output; and providing the primary pathology classification.

Abstract: Computer-implemented techniques for classifying a tissue specimen are presented. The techniques include obtaining an image of the tissue specimen; segmenting the image into a first plurality of segments; selecting a second plurality of segments that include at least one region of interest; applying an electronic convolutional neural network trained by a training corpus including a set of pluralities of tissue sample image segments, each of the pluralities of tissue sample image segments labeled according to one of a plurality of primary pathology classes, where the plurality of primary pathology classes consist of a plurality of majority primary pathology classes, where the plurality of majority primary pathology classes collectively include a majority of pathologies according to prevalence, and a class for tissue sample image segments not in the plurality of majority primary pathology classes, such that a primary pathology classification is output; and providing the primary pathology classification.

Abstract: Techniques for stain normalization image processing for digitized biological tissue images are presented. The techniques include obtaining a digitized biological tissue image; applying to at least a portion of the digitized biological tissue image an at least partially computer implemented convolutional neural network trained using a training corpus including a plurality of pairs of images, where each pair of images of the plurality of pairs of images includes a first image restricted to a lightness axis of a color space and a second image restricted to at least one of: a first color axis of the color space and a second color axis of the color space, such that the applying causes an output image to be produced; and providing the output image.