FQ-UWF: Unpaired Generative Image Enhancement for Fundus Quality Ultra-Widefield Retinal Images.

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.

Learning Controllable ISP for Image Enhancement.

We present a plug-and-play Image Signal Processor (ISP) for image enhancement to better produce diverse image styles than the previous works. Our proposed method, ContRollable Image Signal Processor (CRISP), explicitly controls the parameters of the ISP that determine output image styles. ISP parameters for high-quality (HQ) image styles are encoded into low-dimensional latent codes, allowing fast and easy style adjustments.

Learning to Learn Task-Adaptive Hyperparameters for Few-Shot Learning.

The objective of few-shot learning is to design a system that can adapt to a given task with only few examples while achieving generalization. Model-agnostic meta-learning (MAML), which has recently gained the popularity for its simplicity and flexibility, learns a good initialization for fast adaptation to a task under few-data regime. However, its performance has been relatively limited especially when novel tasks are different from tasks previously seen during training.

A deep learning-based framework for retinal fundus image enhancement.

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.

Concentrations of blood and urinary arsenic species and their characteristics in general Korean population.

Arsenic (As) exposure has been extensively studied by investigating As species (e.g., inorganic arsenic (iAs), monomethylarsonic acid (MMA), and dimethylarsinic acid (DMA)) in urine, yet recent research suggests that blood could be a possible biomarker of As exposure.

Test-Time Adaptation for Video Frame Interpolation via Meta-Learning.

Video frame interpolation is a challenging problem that involves various scenarios depending on the variety of foreground and background motions, frame rate, and occlusion. Therefore, generalizing across different scenes is difficult for a single network with fixed parameters. Ideally, one could have a different network for each scenario, but this will be computationally infeasible for practical applications.

A comparison of meta-analysis results with and without adjustment for the healthy worker effect: cancer mortality among workers in the semiconductor industry.

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).

Toward Real-World Super-Resolution via Adaptive Downsampling Models.

Most image super-resolution (SR) methods are developed on synthetic low-resolution (LR) and high-resolution (HR) image pairs that are constructed by a predetermined operation, e.g., bicubic downsampling.

Learning to Forget for Meta-Learning via Task-and-Layer-Wise Attenuation.

Few-shot learning is an emerging yet challenging problem in which the goal is to achieve generalization from only few examples. Meta-learning tackles few-shot learning via the learning of prior knowledge shared across tasks and using it to learn new tasks. One of the most representative meta-learning algorithms is the model-agnostic meta-learning (MAML), which formulates prior knowledge as a common initialization, a shared starting point from where a learner can quickly adapt to unseen tasks.

Joint segmentation and classification of hepatic lesions in ultrasound images using deep learning.

Objectives: To develop a convolutional neural network system to jointly segment and classify a hepatic lesion selected by user clicks in ultrasound images.

Methods: In total, 4309 anonymized ultrasound images of 3873 patients with hepatic cyst (n = 1214), hemangioma (n = 1220), metastasis (n = 1001), or hepatocellular carcinoma (HCC) (n = 874) were collected and annotated. The images were divided into 3909 training and 400 test images.

Deep vessel segmentation by learning graphical connectivity.

We propose a novel deep learning based system for vessel segmentation. Existing methods using CNNs have mostly relied on local appearances learned on the regular image grid, without consideration of the graphical structure of vessel shape. Effective use of the strong relationship that exists between vessel neighborhoods can help improve the vessel segmentation accuracy.

Tuberculosis infection and lung adenocarcinoma: Mendelian randomization and pathway analysis of genome-wide association study data from never-smoking Asian women.

We investigated whether genetic susceptibility to tuberculosis (TB) influences lung adenocarcinoma development among never-smokers using TB genome-wide association study (GWAS) results within the Female Lung Cancer Consortium in Asia. Pathway analysis with the adaptive rank truncated product method was used to assess the association between a TB-related gene-set and lung adenocarcinoma using GWAS data from 5512 lung adenocarcinoma cases and 6277 controls. The gene-set consisted of 31 genes containing known/suggestive associations with genetic variants from previous TB-GWAS.

Association between family history of diabetes and clusters of adherence to healthy behaviors: cross-sectional results from the Health Examinees-Gem (HEXA-G) study.

Objectives: This study evaluated whether individuals with affected family member adhered to healthy behaviours.

Design And Setting: This 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.

Participants: The baseline data of 128 520 participants enrolled in the Health Examinees-Gem study were used for analysis.

Association between occupational exposure to trichloroethylene and serum levels of microRNAs: a cross-sectional molecular epidemiology study in China.

Objectives: The objective of our study was to evaluate the association between occupational exposure to trichloroethylene (TCE), a suspected lymphomagen, and serum levels of miRNAs in a cross-sectional molecular epidemiology study of TCE-exposed workers and comparable unexposed controls in China.

Methods: Serum levels of 40 miRNAs were compared in 74 workers exposed to TCE (median: 12 ppm) and 90 unexposed control workers. Linear regression models were used to test for differences in serum miRNA levels between exposed and unexposed workers and to evaluate exposure-response relationships across TCE exposure categories using a three-level ordinal variable [i.

Directions for and prospects of the Environmental Health Study in Korean National Industrial Complexes (EHSNIC): A proposal for the third phase of the EHSNIC.

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.

Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images.

We propose a framework for localization and classification of masses in breast ultrasound 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.

Trends and Correlates of High-Risk Alcohol Consumption and Types of Alcoholic Beverages in Middle-Aged Korean Adults: Results From the HEXA-G Study.

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.

Mortality among diagnostic medical radiation workers in South Korea, 1996-2015.

Objectives: To evaluate the risk for all-cause and cause-specific mortality in diagnostic medical radiation workers in South Korea.

Methods: The study population included all diagnostic medical radiation workers enrolled in the National Dosimetry Registry (NDR) between 1996 and 2011. NDR data were linked with mortality data obtained from national registries through 2015.

Longitudinal trends of blood lead levels before and after leaded gasoline regulation in Korea.

The objective of this study was to verify a change in the longitudinal trend of blood lead levels for the Korean population, before and after the regulation of leaded gasoline- which occurred between 1987 and 1993 in Korea. A total of 77 reports on blood lead levels among general Korean population between 1981 and 2014 were selected, and the results were summarized to have the variables of year, number of subjects, the subjects' range in age, gender, and blood lead concentrations (arithmetic mean). The annual average atmospheric lead levels for four major cities (i.

Dynamic Video Deblurring Using a Locally Adaptive Blur Model.

State-of-the-art video deblurring methods cannot handle blurry videos recorded in dynamic scenes since they are built under a strong assumption that the captured scenes are static. Contrary to the existing methods, we propose a new video deblurring algorithm that can deal with general blurs inherent in dynamic scenes. To handle general and locally varying blurs caused by various sources, such as moving objects, camera shake, depth variation, and defocus, we estimate pixel-wise varying non-uniform blur kernels.

Robust Light Field Depth Estimation Using Occlusion-Noise Aware Data Costs.

Depth estimation is essential in many light field applications. Numerous algorithms have been developed using a range of light field properties. However, conventional data costs fail when handling noisy scenes in which occlusion is present.

Urinary levels of phthalate metabolites and associations with demographic characteristics in Korean adults.

The objective of this study is to assess human exposure to phthalates and its associations with demographic characteristics of the subjects in the Korean National Human Biomonitoring Survey. The subjects aged between 18 and 69 were selected through nationwide stratified sampling. A total of 1874 urine samples were collected and stored at -20 °C until measurement for ten selected metabolites of phthalates (MnBP, MiBP, MBzP, MCHP, MnOP, MEHP, MEOHP, MEHHP, MiNP, and MiDP) using high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS).

Adaptive Visual Tracking with Minimum Uncertainty Gap Estimation.

A novel tracking algorithm is proposed, which robustly tracks a target by finding the state that minimizes the likelihood uncertainty. Likelihood uncertainty is estimated by determining the gap between the lower and upper bounds of likelihood. By minimizing the gap between the two bounds, the proposed method identifies the confident and reliable state of the target.

A Novel Cascade Classifier for Automatic Microcalcification Detection.

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.