By applying deep-learning techniques to a set of phase-contrast microscopy images, Japanese researchers have been able to identify the nature and origin of different cancer cells with 96% accuracy. This approach could lead to better cancer treatments (Cancer Res. The researchers, from Osaka University, used a convolutional neural network (CNN), a common scheme used in deep learning, to analyse the images. CNNs work by applying to the input image a set of connected filters and mathematical functions that, similarly to neurons, can be trained to extract specific features. In medical imaging, CNNs are modelled on the human visual system, with low layers that capture fine details such as edges, and higher levels that capture complex features reflecting the whole image.
Scientists from Osaka University in Japan have developed artificial intelligence (AI) that can identify different types of cancers based on microscopy images of their cells. The AI was also able to determine whether the cancer cells were resistant to radiation, and further learned the differences between human and animal cancers. Since the accuracy and timeliness of traditional methods of identifying cancer cells are prone to delays and errors, an accurate and automated system for accomplishing this would be beneficial to cancer research and treatment overall. The results of the scientists' research were published in the December 2018 issue of Cancer Research. The type of AI developed for the cell identification is called a convolutional neural network (CNN); it's loosely based on the connectivity patterns used by neurons in the brain and primarily used for classifying images.