Abstract: The present invention provides various methods for screening one or more compounds, suitably using non-invasive visual methods and neural networks for generating predicted fluorescence images of cells, to assess an effect of the compound on the cell, as well as to classify a compound or to determine an activity of a compound. Also provided are systems and methods for carrying out such assessments.

Abstract: Convolutional neural networks for detecting objects of interest within images of biological specimens are disclosed. Also disclosed are systems and methods of training and using such networks, one method including: obtaining a sample image and at least one of a set of positive points and a set of negative points, wherein each positive point identifies a location of one object of interest within the sample image, and each negative point identifies a location of one object of no-interest within the sample image; obtaining one or more predefined characteristics of objects of interest and/or objects of no-interest, and based on the predefined characteristics, generating a boundary map comprising a positive area around each positive point the set of positive points, and/or a negative area around each negative point in the set of negative points; and training the convolutional neural network using the sample image and the boundary map.

Abstract: Convolutional neural networks for detecting objects of interest within images of biological specimens are disclosed. Also disclosed are systems and methods of training and using such networks, one method including: obtaining a sample image and at least one of a set of positive points and a set of negative points, wherein each positive point identifies a location of one object of interest within the sample image, and each negative point identifies a location of one object of no-interest within the sample image; obtaining one or more predefined characteristics of objects of interest and/or objects of no-interest, and based on the predefined characteristics, generating a boundary map comprising a positive area around each positive point the set of positive points, and/or a negative area around each negative point in the set of negative points; and training the convolutional neural network using the sample image and the boundary map.

Abstract: Convolutional neural networks for detecting objects of interest within images of biological specimens are disclosed. Also disclosed are systems and methods of training and using such networks, one method including: obtaining a sample image and at least one of a set of positive points and a set of negative points, wherein each positive point identifies a location of one object of interest within the sample image, and each negative point identifies a location of one object of no-interest within the sample image; obtaining one or more predefined characteristics of objects of interest and/or objects of no-interest, and based on the predefined characteristics, generating a boundary map comprising a positive area around each positive point the set of positive points, and/or a negative area around each negative point in the set of negative points; and training the convolutional neural network using the sample image and the boundary map.

Abstract: A facility for identifying the boundaries of 3-dimensional structures in 3-dimensional images is described. For each of multiple 3-dimensional images, the facility receives results of a first attempt to identify boundaries of structures in the 3-dimensional image, and causes the results of the first attempt to be presented to a person. For each of a number of 3-dimensional images, the facility receives input generated by the person providing feedback on the results of the first attempt. The facility then uses the following to train a deep-learning network to identify boundaries of 3-dimensional structures in 3-dimensional images: at least a portion of the plurality of 3-dimensional images, at least a portion of the received results, and at least a portion of provided feedback.

Abstract: Convolutional neural networks for detecting objects of interest within images of biological specimens are disclosed. Also disclosed are systems and methods of training and using such networks, one method including: obtaining a sample image and at least one of a set of positive points and a set of negative points, wherein each positive point identifies a location of one object of interest within the sample image, and each negative point identifies a location of one object of no-interest within the sample image; obtaining one or more predefined characteristics of objects of interest and/or objects of no-interest, and based on the predefined characteristics, generating a boundary map comprising a positive area around each positive point the set of positive points, and/or a negative area around each negative point in the set of negative points; and training the convolutional neural network using the sample image and the boundary map.

Abstract: Convolutional neural networks for detecting objects of interest within images of biological specimens are disclosed. Also disclosed are systems and methods of training and using such networks, one method including: obtaining a sample image and at least one of a set of positive points and a set of negative points, wherein each positive point identifies a location of one object of interest within the sample image, and each negative point identifies a location of one object of no-interest within the sample image; obtaining one or more predefined characteristics of objects of interest and/or objects of no-interest, and based on the predefined characteristics, generating a boundary map comprising a positive area around each positive point the set of positive points, and/or a negative area around each negative point in the set of negative points; and training the convolutional neural network using the sample image and the boundary map.

Abstract: A segmentation machine learning model is described that has trained to predict segmentation for images captured in a first manner using training observations that each pair an image of a scene captured in the first manner with a segmentation of an image of the same scene captured in a second manner distinct from the first manner, where the segmentation of the image of the same scene captured in the second manner was produced by applying to the image of the same scene captured in the second manner a model for segmenting images captured in the second manner. The model can be applied to a distinguished image captured in the first manner to predict a segmentation of the distinguished image.

Abstract: A facility for identifying the boundaries of 3-dimensional structures in 3-dimensional images is described. For each of multiple 3-dimensional images, the facility receives results of a first attempt to identify boundaries of structures in the 3-dimensional image, and causes the results of the first attempt to be presented to a person. For each of a number of 3-dimensional images, the facility receives input generated by the person providing feedback on the results of the first attempt. The facility then uses the following to train a deep-learning network to identify boundaries of 3-dimensional structures in 3-dimensional images: at least a portion of the plurality of 3-dimensional images, at least a portion of the received results, and at least a portion of provided feedback.

Abstract: Convolutional neural networks for detecting objects of interest within images of biological specimens are disclosed. Also disclosed are systems and methods of training and using such networks, one method including: obtaining a sample image and at least one of a set of positive points and a set of negative points, wherein each positive point identifies a location of one object of interest within the sample image, and each negative point identifies a location of one object of no-interest within the sample image; obtaining one or more predefined characteristics of objects of interest and/or objects of no-interest, and based on the predefined characteristics, generating a boundary map comprising a positive area around each positive point the set of positive points, and/or a negative area around each negative point in the set of negative points; and training the convolutional neural network using the sample image and the boundary map.