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Ultra-widefield (UWF) retinal imaging stands as a pivotal modality for detecting major eye diseases such as diabetic retinopathy and retinal detachment. However, UWF exhibits a well-documented limitation in terms of low resolution and artifacts in the macular area, thereby constraining its clinical diagnostic accuracy, particularly for macular diseases like age-related macular degeneration. Conventional supervised super-resolution techniques aim to address this limitation by enhancing the resolution of the macular region through the utilization of meticulously paired and aligned fundus image ground truths. However, obtaining such refined paired ground truths is a formidable challenge. To tackle this issue, we propose an unpaired, degradation-aware, super-resolution technique for enhancing UWF retinal images. Our approach leverages recent advancements in deep learning: specifically, by employing generative adversarial networks and attention mechanisms. Notably, our method excels at enhancing and super-resolving UWF images without relying on paired, clean ground truths. Through extensive experimentation and evaluation, we demonstrate that our approach not only produces visually pleasing results but also establishes state-of-the-art performance in enhancing and super-resolving UWF retinal images. We anticipate that our method will contribute to improving the accuracy of clinical assessments and treatments, ultimately leading to better patient outcomes.

The task of 3D human-body reconstruction (3DHR), which mostly utilizes parametric pose and shape representations, has witnessed significant advances in recent years. However, the application of 3DHR techniques in handling real-world in-the-wild data, still faces limitations. Training the 3DHR model in such scenario with 3D human pose’s ground truth (GT) is non-trivial. Curating the accurate 3D human pose GT for in-the-wild scenes remains difficult due to various factors. Recent test-time refinement approaches on 3DHR task leverage 2D off-the-shelf human keypoints information to support the lack of 3D supervision on in-the-wild data. However, we observed that additional 2D supervision alone could cause overfitting issue on common 3DHR backbones, making the 3DHR test-time refinement task seem intractable. We answer this challenge by proposing a strategy that complements 3DHR test-time refinement work under a collaborative approach. Specifically, we initially apply a pre-adaptation approach that works by collaborating various 3DHR models in a single framework to directly improve their initial outputs. This approach is then further combined with the test-time adaptation work under specific settings that minimize the overfitting issue to further boost the 3DHR performance. The whole framework is termed as 3DHR-Co, and on the experiment side, the proposed work can significantly enhance the scores of common classic 3DHR backbones up to -34 mm pose error suppression, putting them among the top list on the in-the-wild benchmark data. Such achievement shows that our approach helps unveil the true potential of the common classic 3DHR backbones. Based on these findings, we further investigate various settings on the proposed framework to better elaborate the capability of our collaborative strategy in the 3DHR task.

In this paper, we propose a novel disease detection framework based on translating a given chest X-ray (CXR) to a corresponding normal CXR image. To train a model for normal CXR translation, we synthesize a paired image dataset from existing public CXR datasets. More specifically, we synthesize new images that appear to be from the same patient but without abnormalities, i.e., matching-pairs, that are not present in the dataset. Here, we search for normal local regions in images contained within the dataset that have a similar appearance, and we apply a blending process to effectively erase the abnormal regions. A conditional GAN can then be trained on the synthetic matching-pair images for a normal CXR translator model that transforms the given CXR images into their corresponding normal CXRs. We incorporate this pretrained translator model into a deep learning based object detection model. Here we define the difference between the translated normal CXR and the given input as the residual-saliency map. As the residual-saliency map will be most active at diseased regions, we use this as the attention within the detection model. We also utilize the matching-pair synthesis pipeline to synthesize abnormal images for data augmentation when we train the detector. We evaluate the proposed approach on two public chest X-ray datasets: RSNA and VinBigData.

We formulate the visual tracking problem as a semi-supervised continual learning problem, where only an initial frame is labeled. In contrast to conventional meta-learning based approaches that regard visual tracking as an instance detection problem with a focus on finding good weights for model initialization, we consider both initialization and online update processes simultaneously under our adaptive continual meta-learning framework. The proposed adaptive meta-learning strategy dynamically generates the hyperparameters needed for fast initialization and online update to achieve more robustness via adaptively regulating the learning process. In addition, our continual meta-learning approach based on knowledge distillation scheme helps the tracker adapt to new examples while retaining its knowledge on previously seen examples. We apply our proposed framework to deep learning-based tracking algorithm to obtain noticeable performance gains and competitive results against recent state-of-the-art tracking algorithms while performing at real-time speeds.

ProblemLow-quality fundus images with complex degredation can cause costly re-examinations of patients or inaccurate clinical diagnosis.AimThis study aims to create an automatic fundus macular image enhancement framework to improve low-quality fundus images and remove complex image degradation.MethodWe 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.ResultsThe 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 4 dB and 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).ConclusionOur 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.

Retinal artery–vein (AV) classification is a prerequisite for quantitative analysis of retinal vessels, which provides a biomarker for neurologic, cardiac, and systemic diseases, as well as ocular diseases. Although convolutional neural networks have presented remarkable performance on AV classification, it often comes with a topological error, like an abrupt class flipping on the same vessel segment or a weakness for thin vessels due to their indistinct appearances. In this paper, we present a new method for AV classification where the underlying vessel topology is estimated to give consistent prediction along the actual vessel structure. We cast the vessel topology estimation as iterative vascular connectivity prediction, which is implemented as deep-learning-based pairwise classification. In consequence, a whole vessel graph is separated into sub-trees, and each of them is classified as an artery or vein in whole via a voting scheme. The effectiveness and efficiency of the proposed method is validated by conducting experiments on two retinal image datasets acquired using different imaging techniques called DRIVE and IOSTAR.

In this paper, we present a novel cascaded classification framework for automatic detection of individual and clusters of microcalcifications (μC). Our framework comprises three classification stages: i) a random forest (RF) classifier for simple features capturing the second order local structure of individual μCs, where non-μC pixels in the target mammogram are efficiently eliminated; ii) a more complex discriminative restricted Boltzmann machine (DRBM) classifier for μC candidates determined in the RF stage, which automatically learns the detailed morphology of μC appearances for improved discriminative power; and iii) a detector to detect clusters of μCs from the individual μC detection results, using two different criteria. From the two-stage RF-DRBM classifier, we are able to distinguish μCs using explicitly computed features, as well as learn implicit features that are able to further discriminate between confusing cases. Experimental evaluation is conducted on the original Mammographic Image Analysis Society (MIAS) and mini-MIAS databases, as well as our own Seoul National University Bundang Hospital digital mammographic database. It is shown that the proposed method outperforms comparable methods in terms of receiver operating characteristic (ROC) and precision-recall curves for detection of individual μCs and free-response receiver operating characteristic (FROC) curve for detection of clustered μCs.

Estimation of the shape dissimilarity between 3D models is a very important problem in both computer vision and graphics for 3D surface reconstruction, modeling, matching, and compression. In this paper, we propose a novel method called surface roving technique to estimate the shape dissimilarity between 3D models. Unlike conventional methods, our surface roving approach exploits a virtual camera and Z-buffer, which is commonly used in 3D graphics. The corresponding points on different 3D models can be easily identified, and also the distance between them is determined efficiently, regardless of the representation types of the 3D models. Moreover, by employing the viewpoint sampling technique, the overall computation can be greatly reduced so that the dissimilarity is obtained rapidly without loss of accuracy. Experimental results show that the proposed algorithm achieves fast and accurate measurement of shape dissimilarity for different types of 3D object models.

We propose a new method to optimize the completely-trained boosted cascade detector on an enforced training set. Recently, due to the accuracy and real-time characteristics of boosted cascade detectors like the Adaboost, a lot of variant algorithms have been proposed to enhance the performance given a fixed number of training data. And, most of algorithms assume that a given training set well exhibits the real world distributions of the target and non-target instances. However, this is seldom true in real situations, and thus often causes higher false-classification ratio. In this paper, to solve the optimization problem of completely trained boosted cascade detector on false-classified instances, we propose a new base hypothesis weight optimization algorithm called DOOMRED (Direct Optimization Of Margin for Rare Event Detection) using a mathematically derived error upper bound of boosting algorithms. We apply the proposed algorithm to a cascade structured frontal face detector trained by AdaBoost algorithm. Experimental results demonstrate that the proposed algorithm has competitive ability to maintain accuracy and real-time characteristic of the boosted cascade detector compared to those of other heuristic approaches while requiring reasonably small amount of optimization time.

In most content-based image retrieval systems, the color information is extensively used for its simplicity and generality. Due to its compactness in characterizing the global information, a uniform quantization of colors, or a histogram, has been the most commonly used color descriptor. However, a cluster-based representation, or a signature, has been proven to be more compact and theoretically sound than a histogram for increasing the discriminatory power and reducing the gap between human perception and computer-aided retrieval system. Despite of these advantages, only few papers have broached dissimilarity measure based on the cluster-based nonuniform quantization of colors. In this paper, we extract the perceptual representation of an original color image, a statistical signature by modifying general color signature, which consists of a set of points with statistical volume. Also we present a novel dissimilarity measure for a statistical signature called Perceptually Modified Hausdorff Distance (PMHD) that is based on the Hausdorff distance. In the result, the proposed retrieval system views an image as a statistical signature, and uses the PMHD as the metric between statistical signatures. The precision versus recall results show that the proposed dissimilarity measure generally outperforms all other dissimilarity measures on an unmodified commercial image database.

OBJECTIVES This study compared the results of meta-analysis with and without adjustment for the healthy worker effect on the association between working in the semiconductor industry and cancer mortality. METHODS Six studies that reported standardized mortality ratios (SMRs) for cancers were selected for meta-analysis. Using a random-effects model, the SMR results from each study were combined for all cancers and leukemias to estimate the summary SMRs (95% confidence interval, CI). To adjust for the healthy worker effect, the relative standardized mortality ratio (rSMR=SMRx/SMRnot x) were calculated using observed and expected counts for the specific cause of interest (i.e., all cancers and leukemias) and the observed and expected counts for all other causes of mortality. Then, the rSMR results were combined to estimate the summary rSMRs (95% CIs). RESULTS The SMRs for all causes of mortality among semiconductor industry workers ranged from 0.25 to 0.80, which reflects a significant healthy worker effect. A remarkable difference was found between the summary SMRs and the summary rSMRs. The summary SMR for all cancers was 0.70 (95% CI, 0.63 to 0.79) whereas the summary rSMR was 1.38 (95% CI, 1.20 to 1.59). The summary SMR for leukemia was 0.88 (95% CI, 0.72 to 1.07), and the summary rSMR was 1.88 (95% CI, 1.20 to 2.95). CONCLUSIONS Our results suggest that adjustment for the healthy worker effect (i.e., rSMR) may be useful in meta-analyses of cohort studies reporting SMRs.

Background: We aimed to report the prevalence and correlates of high-risk alcohol consumption and types of alcoholic beverages. Methods: The baseline data of the Health Examinees-Gem (HEXA-G) study participants, including 43,927 men and 85,897 women enrolled from 2005 through 2013, were used for analysis. Joinpoint regression was performed to estimate trends in the age-standardized prevalence of alcohol consumption. Associations of demographic and behavioral factors, perceived health-related effects, social relationships, and the diagnostic history of diseases with alcohol consumption were assessed using multinomial logistic regression. Results: The prevalence of alcohol consumption remained higher in men during the study period and increased in women. The amount of alcohol consumed has increased in women, especially that from beer and makgeolli, a traditional Korean fermented rice wine. Older participants were less likely to be high-risk drinkers (men and women who drink more than 40 or 20 g/day of alcohol, respectively) and drink Soju, a distilled liquor, and beer, and more likely to drink makgeolli. Educational level was negatively associated with high-risk drinking. However, it was positively associated with the consumption of strong spirits and wine. Smoking was associated with high-risk drinking and the consumption of soju and strong spirits. Engaging in regular exercise and having stress were associated with drinking all types of beverages except for soju. Conclusions: Sex-specific trends in alcohol consumption were influenced by demographic, behavioral, and perceived health-related factors. The findings will help improve the understanding of alcohol-related problems and provide evidence for establishing country-specific policies and campaigns in Korea.

ObjectivesThis study evaluated whether individuals with affected family member adhered to healthy behaviours.Design and settingThis was a cross-sectional study of participants selected from health examinees who underwent the national health check-up programme of Korea in 39 centres between 2004 and 2013.ParticipantsThe baseline data of 128 520 participants enrolled in the Health Examinees-Gem study were used for analysis.Main outcomes and measuresAssociations of family history of diabetes with adherence to regular exercise, healthy diet and body composition, and clusters of healthy behaviours were evaluated while adjusting for potential confounders selected by a directed acyclic graph.ResultsParticipants with a family history of diabetes were more likely to adhere to a regular exercise regimen (OR=1.12, 95% CI 1.06 to 1.18 for men and OR=1.10, 95% CI 1.07 to 1.14 for women) and healthy diet (OR=1.06, 95% CI 1.01 to 1.12 for men and OR=1.06, 95% CI 1.01 to 1.12 for women) but were less likely to have a normal body composition (OR=0.83, 95% CI 0.78 to 0.87 for men and OR=0.83, 95% CI 0.80 to 0.86 for women). These associations were strengthened when the affected family members were siblings, the number of affected members was increased or the age at diagnosis of the affected member was younger than 50 years. In men and women, having a normal body composition is important in determining the cluster of behaviours, and those with a family history of diabetes were less likely to adhere to the normal body composition cluster.ConclusionsThe group with high risk of diabetes showed healthy behaviors, but they did not have a normal body composition. Policies and campaigns targeting integrated health behaviors will be needed to reduce the burden of diseases and improve public health.

The Environmental Health Study in the Korean National Industrial Complexes (EHSNIC) is a project that aims to monitor the exposure and health effects of environmental pollution among residents of national industrial complexes, as well as propose appropriate environmental health measures. Since its launch in 2003, this project has been initiated in eight national industrial complexes. Currently, it is necessary to review the accomplishments and limitations of the phases 1 and 2 of this project, and establish the direction of the upcoming the phase 3. Thus, the present study has developed principles and goals for the phase 3, considering the rationale and justification of the EHSNIC, and presented specific research contents accordingly. In the phase 3, it is important to improve the methods for exposure assessment and evaluation of health effects, in order to identify clearly the association between the pollutants released from industrial complexes and their health impacts, to develop and to reinforce communication strategies to promote participation of residents of communities near industrial complexes. Nonetheless, it is also important to maintain the basic goal of continuously monitoring the level of exposure to and health effects of environmental pollutants.

In South Korea, a cluster of acute lung disease patients included lung injury disease suspected of being caused by the use of humidifier disinfectants. We examined the relationship between humidifier disinfectant exposure and clinically diagnosed humidifier disinfectant-associated lung injury (HDLI) in a family-based study.This case-control study included 169 clinically confirmed HDLI cases and 303 family controls who lived with the HDLI patients. A range of information on exposure to humidifier disinfectants was obtained using a structured questionnaire and field investigations. Odds ratios (ORs) and confidence intervals (CIs) were estimated using unconditional logistic regression models that were adjusted for age, sex, presence of a factory within 1 km of residence, and the number of household chemical products used.HDLI risk increased approximately two-fold or more among the highest quartile compared with the lowest quartile in terms of the hours sleeping in a room with an operating humidifier treated with disinfectant (adjusted OR = 2.0, 95 % CI = 1.1-3.7), average hours of disinfectant-treated humidifier use per day (adjusted OR = 2.1, 95 % CI = 1.0-4.5), airborne disinfectant intensity (adjusted OR = 2.6, 95% CI = 1.2-5.3), and cumulative disinfectant inhalation level (adjusted OR = 2.0, 95% CI = 1.0-4.1). HDLI risk increased as the distance of the bed from humidifier gets shorter; compared with longer distance (> 1 m), the odds ratio was 2.7 for 0.5 to 1 m (95 % CI = 1.5-5.1) and 13.2 for

Genome-wide association studies of lung cancer reported in populations of European background have identified three regions on chromosomes 5p15.33, 6p21.33, and 15q25 that have achieved genome-wide significance with p-values of 10(-7) or lower. These studies have been performed primarily in cigarette smokers, raising the possibility that the observed associations could be related to tobacco use, lung carcinogenesis, or both. Since most women in Asia do not smoke, we conducted a genome-wide association study of lung adenocarcinoma in never-smoking females (584 cases, 585 controls) among Han Chinese in Taiwan and found that the most significant association was for rs2736100 on chromosome 5p15.33 (p = 1.30 x 10(-11)). This finding was independently replicated in seven studies from East Asia totaling 1,164 lung adenocarcinomas and 1,736 controls (p = 5.38 x 10(-11)). A pooled analysis achieved genome-wide significance for rs2736100. This SNP marker localizes to the CLPTM1L-TERT locus on chromosome 5p15.33 (p = 2.60 x 10(-20), allelic risk = 1.54, 95% Confidence Interval (CI) 1.41-1.68). Risks for heterozygote and homozygote carriers of the minor allele were 1.62 (95% CI; 1.40-1.87), and 2.35 (95% CI: 1.95-2.83), respectively. In summary, our results show that genetic variation in the CLPTM1L-TERT locus of chromosome 5p15.33 is directly associated with the risk of lung cancer, most notably adenocarcinoma.