Abstract: A neural network is trained using transfer learning to analyze medical image data, including 2D, 3D, and 4D images and models. Where the target medical image data is associated with a species or problem class for which there is not sufficient labeled data available for training, the system may create enhanced training datasets by selecting labeled data from other species, and/or labeled data from different problem classes. During training and analysis, image data is chunked into portions that are small enough to obfuscate the species source, while being large enough to preserve meaningful context related to the problem class (e.g., the image portion is small enough that it can't be determined whether it is from a human or canine, but abnormal liver tissues are still identifiable). A trained checkpoint may then be used to provide automated analysis and heat mapping of input images via a cloud platform or other application.

Abstract: A neural network is trained using transfer learning to analyze medical image data, including 2D, 3D, and 4D images and models. Where the target medical image data is associated with a species or problem class for which there is not sufficient labeled data available for training, the system may create enhanced training datasets by selecting labeled data from other species, and/or labeled data from different problem classes. During training and analysis, image data is chunked into portions that are small enough to obfuscate the species source, while being large enough to preserve meaningful context related to the problem class (e.g., the image portion is small enough that it can't be determined whether it is from a human or canine, but abnormal liver tissues are still identifiable). A trained checkpoint may then be used to provide automated analysis and heat mapping of input images via a cloud platform or other application.

Abstract: A neural network is trained using transfer learning to analyze medical image data, including 2D, 3D, and 4D images and models. Where the target medical image data is associated with a species or problem class for which there is not sufficient labeled data available for training, the system may create enhanced training datasets by selecting labeled data from other species, and/or labeled data from different problem classes. During training and analysis, image data is chunked into portions that are small enough to obfuscate the species source, while being large enough to preserve meaningful context related to the problem class (e.g., the image portion is small enough that it can't be determined whether it is from a human or canine, but abnormal liver tissues are still identifiable). A trained checkpoint may then be used to provide automated analysis and heat mapping of input images via a cloud platform or other application.