Abstract: A convolutional neural network predicts which regions of a tissue slice would be stained by a first stain by training a model to identify those regions based only on tissue stained by a second stain. Thereafter the first stain need not be used to mark cancerous regions on other tissue slices that are stained with the second stain. The training slice is stained with a first immunohistochemical stain and a second counterstain. A target region of an image of the training slice is identified using image analysis based on the first stain. A set of parameters for associated mathematical operations are optimized to train the model to classify pixels of the image as belonging to the target region based on the second stain but not on the first stain. The trained parameters are stored in a database and applied to other images of tissue not stained with the first stain.

Abstract: A convolutional neural network predicts which regions of a tissue slice would be stained by a first stain by training a model to identify those regions based only on tissue stained by a second stain. Thereafter the first stain need not be used to mark cancerous regions on other tissue slices that are stained with the second stain. The training slice is stained with a first immunohistochemical stain and a second counterstain. A target region of an image of the training slice is identified using image analysis based on the first stain. A set of parameters for associated mathematical operations are optimized to train the model to classify pixels of the image as belonging to the target region based on the second stain but not on the first stain. The trained parameters are stored in a database and applied to other images of tissue not stained with the first stain.

Abstract: A convolutional neural network predicts which regions of a tissue slice would be stained by a first stain by training a model to identify those regions based only on tissue stained by a second stain. Thereafter the first stain need not be used to mark cancerous regions on other tissue slices that are stained with the second stain. The training slice is stained with a first immunohistochemical stain and a second counterstain. A target region of an image of the training slice is identified using image analysis based on the first stain. A set of parameters for associated mathematical operations are optimized to train the model to classify pixels of the image as belonging to the target region based on the second stain but not on the first stain. The trained parameters are stored in a database and applied to other images of tissue not stained with the first stain.

Abstract: A convolutional neural network predicts which regions of a tissue slice would be stained by a first stain by training a model to identify those regions based only on tissue stained by a second stain. Thereafter the first stain need not be used to mark cancerous regions on other tissue slices that are stained with the second stain. The training slice is stained with a first immunohistochemical stain and a second counterstain. A target region of an image of the training slice is identified using image analysis based on the first stain. A set of parameters for associated mathematical operations are optimized to train the model to classify pixels of the image as belonging to the target region based on the second stain but not on the first stain. The trained parameters are stored in a database and applied to other images of tissue not stained with the first stain.