An analysis of robust cost functions for CNN in computer-aided diagnosis. Issue 3 (4th May 2018)

• Record Type: Journal Article
• Authors: Barbu, Adrian; Lu, Le; Roth, Holger; Seff, Ari; Summers, Ronald M.
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 6:Issue 3(2018)
• Page Start: 253
• Page End: 258
• Publication Date: 2018-05-04
• Subjects: Lymph node detection -- convolutional neural networks -- computer aided diagnosis; Imaging systems in biology -- Periodicals; Imaging systems in medicine -- Periodicals; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; 616.0757
• DOI: 10.1080/21681163.2016.1138240
• Languages: English
• ISSNs: 2168-1163

Abstract

Abstract : Deep convolutional neural networks (CNNs) have proven to be powerful and flexible tools that advance the state-of-the-art in many fields, e.g. speech recognition, computer vision and medical imaging. Usually deep CNN models employ the logistic (soft-max) loss function in the training process of classification tasks. Recent evidence on a computer vision benchmark data-set indicates that the hinge (SVM) loss might give smaller misclassification errors on the test set compared to the logistic loss (i.e. offer better generality). In this paper, we study and compare four different loss functions for deep CNNs in the context of computer-aided abdominal and mediastinal lymph node detection and diagnosis (CAD) using CT images. Besides the logistic loss, we compare three other CNN losses that have not been previously studied for CAD problems. The experiments confirm that the logistic loss performs the worst among the four losses, and an additional 3% increase in detection rate at 3 false positives/volume can be obtained by just replacing it with Lorenz loss. The free-receiver operating characteristic curves of two of the three loss functions consistently outperform the logistic loss in testing.