Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing cell images using neural networks. One of the methods includes obtaining data comprising an input image of one or more biological cells illuminated with an optical microscopy technique; processing the data using a stained cell neural network; and processing the one or more stained cell images using a cell characteristic neural network, wherein the cell characteristic neural network has been configured through training to receive the one or more stained cell images and to process the one or more stained cell images to generate a cell characteristic output that characterizes features of the biological cells that are stained in the one or more stained cell images.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: obtaining data comprising an input image of one or more biological cells illuminated with an optical microscopy technique; processing the data using a stained cell neural network, wherein the stained cell neural network has been configured through training to receive the data comprising the input image of the one or more biological cells illuminated with the optical microscopy technique and to process the data to generate one or more stained cell images, wherein each stained cell image depicts a respective stained version of the one or more biological cells in the input image; and processing the one or more stained cell images using a cell characteristic neural network, wherein the cell characteristic neural network has been configured through training to receive the one or more stained cell images and to process the one or more stained cell images to generate a cell characteristic output that characterizes features of the biological cells that are stained in the one or more stained cell images. 2. The method of claim 1, wherein the optical microscopy technique is one of: differential interference contrast microscopy, red light microscopy, or ultraviolet light microscopy. 3. The method of claim 1, wherein the stained cell image has a fluorescent channel. 4. The method of claim 1, wherein each pixel in each of the one or more stained cell images corresponds to a pixel in the input image of the one or more biological cells. 5. The method of claim 1, wherein the cell characteristic output comprises data characterizing one or more of a cell type, a cell health, a cell population, a cell phenotypes, or a cell state of differentiation in induced pluripotent stem cell protocols of the one or more biological cells that are stained in the one or more stained cell images. 6. The method of claim 1, wherein the stained cell neural network implements fully convolutional image-to-image regression and the data comprising the image of the one or more biological cells is foveated. 7. The method of claim 1, wherein the cell characteristic output specifies an average cell health or a percentage of cells that have correctly differentiated through induced pluripotent stem cell protocols. 8. The method of claim 1, wherein the cell characteristic output specifies a cell condition for each biological cell of the one or more biological cells. 9. The method of claim 1, wherein the data comprising the image of the one or more biological cells further comprises additional images of the one or more biological cells, wherein each of the additional images have a different focal point for the image of the one or more biological cells. 10. A system comprising one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations comprising: obtaining data comprising an input image of one or more biological cells illuminated with an optical microscopy technique; processing the data using a stained cell neural network, wherein the stained cell neural network has been configured through training to receive the data comprising the input image of the one or more biological cells illuminated with the optical microscopy technique and to process the data to generate one or more stained cell images, wherein each stained cell image depicts a respective stained version of the one or more biological cells in the input image; and processing the one or more stained cell images using a cell characteristic neural network, wherein the cell characteristic neural network has been configured through training to receive the one or more stained cell images and to process the one or more stained cell images to generate a cell characteristic output that characterizes features of the biological cells that are stained in the one or more stained cell images. 11. The system of claim 10, wherein the optical microscopy technique is one of: differential interference contrast microscopy, red light microscopy, or ultraviolet light microscopy. 12. The system of claim 10, wherein the stained cell image has a fluorescent channel. 13. The system of claim 10, wherein each pixel in each of the one or more stained cell images corresponds to a pixel in the input image of the one or more biological cells. 14. The system of claim 10, wherein the cell characteristic output comprises data characterizing one or more of a cell type, a cell health, a cell population, a cell phenotypes, or a cell state of differentiation in induced pluripotent stem cell protocols of the one or more biological cells that are stained in the one or more stained cell images. 15. The system of claim 10, wherein the stained cell neural network implements fully convolutional image-to-image regression and the data comprising the image of the one or more biological cells is foveated. 16. The system of claim 10, wherein the cell characteristic output specifies an average cell health or a percentage of cells that have correctly differentiated through induced pluripotent stem cell protocols. 17. The system of claim 10, wherein the cell characteristic output specifies a cell condition for each biological cell of the one or more biological cells. 18. The system of claim 10, wherein the data comprising the image of the one or more biological cells further comprises additional images of the one or more biological cells, wherein each of the additional images have a different focal point for the image of the one or more biological cells. 19. One or more non-transitory storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: obtaining data comprising an input image of one or more biological cells illuminated with an optical microscopy technique; processing the data using a stained cell neural network, wherein the stained cell neural network has been configured through training to receive the data comprising the input image of the one or more biological cells illuminated with the optical microscopy technique and to process the data to generate one or more stained cell images, wherein each stained cell image depicts a respective stained version of the one or more biological cells in the input image; and processing the one or more stained cell images using a cell characteristic neural network, wherein the cell characteristic neural network has been configured through training to receive the one or more stained cell images and to process the one or more stained cell images to generate a cell characteristic output that characterizes features of the biological cells that are stained in the one or more stained cell images. 20. The non-transitory storage media of claim 19, wherein the cell characteristic output comprises data characterizing one or more of a cell type, a cell health, a cell population, a cell phenotypes, or a cell state of differentiation in induced pluripotent stem cell protocols of the one or more biological cells that are stained in the one or more stained cell images.