A research team has created a system that uses a convolutional neural network to learn the features distinguishing different cancer cells, based on images from a phase-contrast microscope. This system accurately differentiated human and mouse cancer cells, as well as their radioresistant clones. This novel approach can improve the speed and accuracy of cancer diagnosis by avoiding the laboriousness and potential errors associated with equivalent analyses by humans.