False Positive Reduction by Actively Mining Negative Samples for Pulmonary Nodule Detection in Chest Radiographs
AbstractWhile CADe(Computer aided detection) systems can achieve high sensitivity, their relatively low specificity has limited its implementation in the clinical setting. One of the major limiting factors for false positive reduction is the lack of good quality labeled data(with lesion labels). Our approach to solving this problem was utilizing unlabeled data (with unknown lesion and class labels), which tends to be more readily available. The goal of this study is to develop a semi-supervised learning method, that allows us to find pseudo-negative labeled data from unlabeled data and use this to improve the specificity of the detection task. We will then compare this to the false positive reduction achieved using clinically verified negative data, which is the theoretical optimum within our model and data setting.
Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks
AbstractRetinal vessel segmentation is an indispensable step for automatic detection of retinal diseases with fundoscopic images. Though many approaches have been proposed, existing methods tend to miss fine vessels or allow false positives at terminal branches. Let alone under-segmentation, over-segmentation is also problematic when quantitative studies need to measure the precise width of vessels. In this paper, we present a method that generates the precise map of retinal vessels using generative adversarial training. Our methods achieve dice coefficient of 0.829 on DRIVE dataset and 0.834 on STARE dataset which is the state-of-the-art performance on both datasets.
American Journal of Roentgenology 2017
Computer-Assisted Program using Deep Learning Technique in Determination of Bone Age: Evaluation of the Accuracy and Efficiency
AbstractTo evaluate the accuracy of the new automatic software system for bone age assessment and validate its feasibility in clinical practice.
A Comparative Study of Automatic Hand Bone Age Assessment Systems
AbstractThe bone age assessment is a critical procedure in pediatric radiology for diagnosis of many disorders and assessment of response to treatment. It can be performed by either Greulich and Pyle(GP) or Tanner- Whitehouse(TW) method. Although GP method is convenient to use, it can lead to subjective results. To overcome these limitations, there have been a few trials for automated bone age assessment including commercialized system. To verify the validity of these attempts, we developed Korean based bone age assessment system based on convolutional neural network (CNN) and compared our system with the commercialized system(CS) and Harvard's system(HS).