A computer system which harnesses the power of AI to learn the “language of cancer” could help provide faster diagnosis, its developers have said. Researchers said the system is capable of spotting the signs of the disease in biological samples with remarkable accuracy, and can also provide reliable predictions of patient outcomes. Currently, pathologists examine and characterise the features of tissue samples taken from cancer patients on slides under a microscope.
Their observations on the tumour’s type and stage of growth help doctors determine each patient’s course of treatment and their chances of recovery. An international team of AI specialists and cancer scientists, led by researchers from the University of Glasgow and New York University , have developed a new system, which they call histomorphological phenotype learning (HPL). They began by collecting thousands of high-resolution images of tissue samples of lung adenocarcinoma taken from 452 patients stored in the United States National Cancer Institute’s Cancer Genome Atlas database.
In many cases, the data is accompanied by additional information on how the patients’ cancers progressed. Next, they developed an algorithm which used a training process called self-supervised deep learning to analyse the images and spot patterns based solely on the visual data in each slide. The algorithm broke down the slide images into thousands of tiny tiles, each representing a small amount of human tissue.
A deep neur.
