Abstract: Provided herein are methods of classifying lesions in medical image of subjects in certain embodiments. Related systems and computer program products are also provided.

Abstract: Provided herein are methods of generating models to predict prospective pathology scores of test subjects having a pathology in certain embodiments. Related systems and computer program products are also provided.

Abstract: Provided herein are methods of generating models to predict prospective pathology scores of test subjects having a pathology in certain embodiments. Related systems and computer program products are also provided.

Abstract: Provided herein are methods of generating medical images that include the use of a generative adversarial network (GAN) in certain embodiments. Related methods, systems, and computer program products are also provided.

Abstract: Provided herein are methods of generating models to predict prospective pathology scores of test subjects having a pathology in certain embodiments. Related systems and computer program products are also provided.

Abstract: Techniques are provided for determining confident data samples for machine learning (ML) models on unseen data. In one embodiment, a method is provided that comprises extracting, by a system comprising a processor, a feature vector for a data sample based on projection of the data sample onto a standard feature space. The method further comprises processing, by the system, the feature vector using an outlier detection model to determine whether the data sample is within a scope of a training dataset used to train a machine learning model, wherein the outlier detection model was trained using features extracted from the training dataset based on projection of data samples included in the training dataset onto the standard feature space.

Abstract: Techniques are provided for determining confident data samples for machine learning (ML) models on unseen data. In one embodiment, a method is provided that comprises extracting, by a system comprising a processor, a feature vector for a data sample based on projection of the data sample onto a standard feature space. The method further comprises processing, by the system, the feature vector using an outlier detection model to determine whether the data sample is within a scope of a training dataset used to train a machine learning model, wherein the outlier detection model was trained using features extracted from the training dataset based on projection of data samples included in the training dataset onto the standard feature space.