Computer-aided diagnosis in the era of deep learning

Abstract

Computer-aided diagnosis (CAD) has been a major field of research for the past few decades. CAD uses machine learning methods to analyze imaging and/or nonimaging patient data and makes assessment of the patient's condition, which can then be used to assist clinicians in their decision-making process. The recent success of the deep learning technology in machine learning spurs new research and development efforts to improve CAD performance and to develop CAD for many other complex clinical tasks. In this paper, we discuss the potential and challenges in developing CAD tools using deep learning technology or artificial intelligence (AI) in general, the pitfalls and lessons learned from CAD in screening mammography and considerations needed for future implementation of CAD or AI in clinical use. It is hoped that the past experiences and the deep learning technology will lead to successful advancement and lasting growth in this new era of CAD, thereby enabling CAD to deliver intelligent aids to improve health care.

Keywords: artificial intelligence; computer-aided diagnosis; deep learning.

Fig. 1.

ROI-based area under the receiver operating characteristic curve (AUC) performance on the DBT test set while varying the simulated mammography sample size available for training. The data point and the upper and lower range show the mean and standard deviation of the test AUC resulting from ten random samplings of the training set of a given size from the original set. “A. Stage 1 (MAM:C₁)” denotes single-stage training using mammography data and the C₁-layer of the ImageNet pre-trained AlexNet frozen during transfer learning without stage 2. “B. Stage 2 (DBT:C₁)” denotes stage 2 C₁-frozen transfer learning at a fixed (100%) DBT training set size after stage 1 transfer learning (curve A). “C. Stage 2 (DBT:C₁-F₄)” denotes stage 2 C₁-to-F₄-frozen transfer learning at a fixed (100%) DBT training set size after stage 1 transfer learning (curve A). [reprint with permission]

Fig. 2.

ROI-based AUC performance on the DBT test set while varying the simulated DBT sample size available for training. The data point and the upper and lower range show the mean and standard deviation of the test AUC resulting from ten random samplings of the DBT training set of a given size from the original set. “D. Stage 1 (DBT:C₁)” denotes single-stage training using DBT training set with the C₁-layer of the ImageNet pre-trained AlexNet frozen during transfer learning without stage 2. “B. Stage 2 (DBT:C₁)” denotes stage 2 C₁-frozen transfer learning after stage 1 transfer learning with a fixed (100%) mammography training set. “C. Stage 2 (DBT:C₁-F₄)” denotes stage 2 C₁-to-F₄-frozen transfer learning after stage 1 transfer learning with a fixed (100%) mammography training set. [reprint with permission]