We propose a framework for localization and classification of masses in breast ultrasound (BUS) images. We have experimentally found that training convolutional neural network based mass detectors with large, weakly annotated datasets presents a non-trivial problem, while overfitting may occur with those trained with small, strongly annotated datasets. To overcome these problems, we use a weakly annotated dataset together with a smaller strongly annotated dataset in a hybrid manner. We propose a systematic weakly and semi-supervised training scenario with appropriate training loss selection. Experimental results show that the proposed method can successfully localize and classify masses with less annotation effort. The results trained with only 10 strongly annotated images along with weakly annotated images were comparable to results trained from 800 strongly annotated images, with the 95% confidence interval of difference -3.00%–5.00%, in terms of the correct localization (CorLoc) measure, which is the ratio of images with intersection over union with ground truth higher than 0.5. With the same number of strongly annotated images, additional weakly annotated images can be incorporated to give a 4.5% point increase in CorLoc, from 80.00% to 84.50% (with 95% confidence intervals 76.00%–83.75% and 81.00%–88.00%). The effects of different algorithmic details and varied amount of data are presented through ablative analysis.

Fingerprints are known to be easily synthesized to trick identification systems. In this paper, we propose a new method that incorporates template fingerprints stored for identification in the liveness detection system. The fingerprint identification platform must have a list of template fingerprints stored for matching with new probe fingerprints trying to access the system. Thus, instead of simply detecting the liveness of the probe fingerprints, the proposed approach uses the matching template fingerprints along with probe fingerprints through convolutional neural networks to make the liveness decision, which comprises two sequential convolutional neural networks for classification. The proposed method can be built on the top of existing liveness detection methods to increase accuracy without a significant increase in computation time. The evaluation over the LivDet dataset shows that the proposed fingerprint liveness detection method is able to obtain state-of-the-art accuracy.

Problem: Low-quality fundus images with complex degredation can cause costly re-examinations of patients or inaccurate clinical diagnosis.Aim: This study aims to create an automatic fundus macular image enhancement framework to improve low-quality fundus images and remove complex image degradation.Method: We propose a new deep learning-based model that automatically enhances low-quality retinal fundus images that suffer from complex degradation. We collected a dataset, comprising 1068 pairs of high-quality (HQ) and low-quality (LQ) fundus images from the Kangbuk Samsung Hospital’s health screening program and ophthalmology department from 2017 to 2019. Then, we used these dataset to develop data augmentation methods to simulate major aspects of retinal image degradation and to propose a customized convolutional neural network (CNN) architecture to enhance LQ images, depending on the nature of the degradation. Peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM),r-value (linear index of fuzziness), and proportion of ungradable fundus photographs before and after the enhancement process are calculated to assess the performance of proposed model. A comparative evaluation is conducted on an external database and four different open-source databases.Results: The results of the evaluation on the external test dataset showed an significant increase in PSNR and SSIM compared with the original LQ images. Moreover, PSNR and SSIM increased by over 4dBand 0.04, respectively compared with the previous state-of-the-art methods (P< 0.05). The proportion of ungradable fundus photographs decreased from 42.6% to 26.4% (P= 0.012).Conclusion: Our enhancement process improves LQ fundus images that suffer from complex degradation significantly. Moreover our customized CNN achieved improved performance over the existing state-of-the-art methods. Overall, our framework can have a clinical impact on reducing re-examinations and improving the accuracy of diagnosis.

Motion artifacts may occur in coronary computed tomography angiography (CCTA) due to the heartbeat and impede the clinicianb’s diagnosis of coronary arterial diseases. Thus, motion artifact correction of the coronary artery is required to quantify the risk of disease more accurately. We present a novel method based on deep learning for motion artifact correction in CCTA. Because the image of the coronary artery without motion (the ground-truth data required in supervised deep learning) is medically unattainable, we apply a style transfer method to 2D image patches cropped from full-phase 4D computed tomography (CT) to synthesize these images. We then train a convolutional neural network (CNN) for motion artifact correction using this synthetic ground-truth (SynGT). During testing, the output motion-corrected 2D image patches of the trained network are reinserted into the 3D CT volume with volumetric interpolation. The proposed method is evaluated using both phantom and clinical data. A phantom study demonstrates comparable results to other methods in quantitative performance and outperforms those methods in computation time. For clinical data, a quantitative analysis based on metric measurements is presented that confirms the correction of motion artifacts. Moreover, an observer study finds that by applying the proposed method, motion artifacts are markedly reduced, and boundaries of the coronary artery are much sharper, with a strong inter-observer agreement (κ=0.78). Finally, evaluations using commercial software on the original and resulting CT volumes of the proposed method reveal a considerable increase in tracked coronary artery length.