Deep Learning for Cancer Screening in Medical Imaging

Cancer screening in medical imaging is one of the most important areas in computerized medical software. Especially, attempts to automate the early diagnosis of cancer using computer aided detection (CAD) algorithm on chest X-ray and mammography images were the most important research topic in the field of radiology [1]. However, the results of the clinical effects of CAD are still controversial. Even there was a research about screening performance of CAD reporting that sensitivity was significantly decreased for mammograms interpreted with vs without CAD in the subset of radiologists who interpreted both with and without CAD (odds ratio, 0.53; 95% CI, 0.29-0.97) [2]. But, deep learning technology, which has recently been greatly developed, is raising expectations for the possibility of computer software related to cancer screening again. Deep learning is a kind of neural network. The neural network consists of an input layer, a hidden layer, and an output layer. Deep learning is a neural network with a large number of hidden layers. Over the past few years, deep learning has achieved tremendous performance improvements, especially in image classification [3] and speech recognition [4]. In recent Corresponding Author: Jihoon Jeong Advisor, Lunit Inc., 6th Floor, 175 Yeoksamro, Gangnam-gu, Seoul, Korea Tel: +82-10-2512-2540 E-mail: jjeong@lunit.io