Deep learning-enabled medical computer vision
March 1, 2022
Computer vision (CV) has a rich history spanning decades1 of efforts to enable computers to perceive visual stimuli meaningfully. Machine perception spans a range of levels, from lowlevel tasks such as identifying edges, to high-level tasks such as understanding complete scenes. Advances in the last decade have largely been due to three factors: (1) the maturation of deep learning (DL)—a type of machine learning that enables end-toend learning of very complex functions from raw data2 (2) strides in localized compute power via GPUs3 , and (3) the open-sourcing of large labeled datasets with which to train these algorithms4 . The combination of these three elements has enabled individual researchers the resource access needed to advance the field. As the research community grew exponentially, so did progress. The growth of modern CV has overlapped with the generation of large amounts of digital data in a number of scientific fields. Recent medical advances have been prolific 5,6 , owing largely to DL’s remarkable ability to learn many tasks from most data sources. Using large datasets, CV models can acquire many pattern-recognition abilities—from physician-level diagnostics7 to medical scene perception8
