Abstract: The present disclosure provides methods and systems for classifying and/or monitoring a cancer of a subject. A method for assessing a cancer of a subject may comprise obtaining a data set comprising image and/or tabular data from the subject and processing the data with one or more trained algorithms to classify the cancer of the subject. The cancer of the subject may be assessed based on the results of the classification. The assessment may comprise determining a biomarker predictive of a response to a therapeutic intervention for treating the cancer of the subject.

Abstract: The present disclosure provides methods and systems for classifying and/or monitoring a cancer of a subject. A method for assessing a cancer of a subject may comprise obtaining a data set comprising image and/or tabular data from the subject and processing the data with one or more trained algorithms to classify the cancer of the subject. The cancer of the subject may be assessed based on the results of the classification.

Abstract: The present disclosure provides methods and systems for classifying and/or monitoring a cancer of a subject. A method for assessing a cancer of a subject may comprise obtaining a data set comprising image and/or tabular data from the subject and processing the data with one or more trained algorithms to classify the cancer of the subject. The cancer of the subject may be assessed based on the results of the classification. The assessment may comprise determining a biomarker predictive of a response to a therapeutic intervention for treating the cancer of the subject.

Abstract: The present disclosure provides methods and systems for classifying and/or monitoring a cancer of a subject. A method for assessing a cancer of a subject may comprise obtaining a data set comprising image and/or tabular data from the subject and processing the data with one or more trained algorithms to classify the cancer of the subject. The cancer of the subject may be assessed based on the results of the classification.

Abstract: An AI-enhanced data labeling tool assists a human operator in annotating image data. The tool may use a segmentation model to identify portions to be labeled. Initially, the operator manually annotates portions and once the operator has labeled a sufficient number of portions, a classifier is trained to predict labels for other portions. The predictions generated by the classifier are presented to the operator for approval or modification. The tool may also include an active learning model that recommends portions of the image data for the operator to annotate next. The active learning model may suggest one or more batches of portions based on the extent to which, once labeled, those batches will increase the diversity of the total set of labeled portions.

Abstract: Some embodiments of the current disclosure disclose methods and systems for batch active learning using the Shapley values of data points. In some embodiments, Shapley values of a first subset of labeled data are used to measure the contributions of the first subset of data to the performance of neural network. Further, a regression model that correlates the first subset of data to their Shapley values is trained to predict the Shapley values of a second subset of data that are unlabeled. A portion of the second subset of data may then be selected for labeling based on the predicted Shapley values.

Abstract: An AI-enhanced data labeling tool assists a human operator in annotating image data. The tool may use a segmentation model to identify portions to be labeled. Initially, the operator manually annotates portions and once the operator has labeled a sufficient number of portions, a classifier is trained to predict labels for other portions. The predictions generated by the classifier are presented to the operator for approval or modification. The tool may also include an active learning model that recommends portions of the image data for the operator to annotate next. The active learning model may suggest one or more batches of portions based on the extent to which, once labeled, those batches will increase the diversity of the total set of labeled portions.

Abstract: Some embodiments of the current disclosure disclose methods and systems for batch active learning using the Shapley values of data points. In some embodiments, Shapley values of a first subset of labeled data are used to measure the contributions of the first subset of data to the performance of neural network. Further, a regression model that correlates the first subset of data to their Shapley values is trained to predict the Shapley values of a second subset of data that are unlabeled. A portion of the second subset of data may then be selected for labeling based on the predicted Shapley values.