Regression-free Blind Image Quality Assessment

Regression-based blind image quality assessment (IQA) models are susceptible to biased training samples, leading to a biased estimation of model parameters. To mitigate this issue, we propose a regression-free framework for image quality evaluation, which is founded upon retrieving similar instances by incorporating semantic and distortion features. The motivation behind this approach is rooted in the observation that the human visual system (HVS) has analogous visual responses to semantically similar image contents degraded by the same distortion. The proposed framework comprises two classification-based modules: semantic-based classification (SC) module and distortion-based classification (DC) module. Given a test image and an IQA database, the SC module retrieves multiple pristine images based on semantic similarity. The DC module then retrieves instances based on distortion similarity from the distorted images that correspond to each retrieved pristine image. Finally, the predicted quality score is derived by aggregating the subjective quality scores of multiple retrieved instances. Experimental results on four benchmark databases validate that the proposed model can remarkably outperform the state-of-the-art regression-based models.Comment: 11 pages, 7 figures, 50 conference

Deep Gaussian Denoiser Epistemic Uncertainty and Decoupled Dual-Attention Fusion

Following the performance breakthrough of denoising networks, improvements have come chiefly through novel architecture designs and increased depth. While novel denoising networks were designed for real images coming from different distributions, or for specific applications, comparatively small improvement was achieved on Gaussian denoising. The denoising solutions suffer from epistemic uncertainty that can limit further advancements. This uncertainty is traditionally mitigated through different ensemble approaches. However, such ensembles are prohibitively costly with deep networks, which are already large in size. Our work focuses on pushing the performance limits of state-of-the-art methods on Gaussian denoising. We propose a model-agnostic approach for reducing epistemic uncertainty while using only a single pretrained network. We achieve this by tapping into the epistemic uncertainty through augmented and frequency-manipulated images to obtain denoised images with varying error. We propose an ensemble method with two decoupled attention paths, over the pixel domain and over that of our different manipulations, to learn the final fusion. Our results significantly improve over the state-of-the-art baselines and across varying noise levels.Comment: Code and models are publicly available on https://github.com/IVRL/DE

Resilience to Plasma and Cerebrospinal Fluid Amyloid-β in Cognitively Normal Individuals: Findings From Two Cohort Studies.

Objective: To define resilience metrics for cognitive decline based on plasma and cerebrospinal fluid (CSF) amyloid-β (Aβ) and examine the demographic, genetic, and neuroimaging factors associated with interindividual differences among metrics of resilience and to demonstrate the ability of such metrics to predict the diagnostic conversion to mild cognitive impairment (MCI). Methods: In this study, cognitively normal (CN) participants with Aβ-positive were included from the Sino Longitudinal Study on Cognitive Decline (SILCODE, n = 100) and Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 144). Using a latent variable model of data, metrics of resilience [brain resilience (BR), cognitive resilience (CR), and global resilience (GR)] were defined based on the plasma Aβ and CSF Aβ. Linear regression analyses were applied to investigate the association between characteristics of individuals (age, sex, educational level, genetic, and neuroimaging factors) and their resilience. The plausibility of these metrics was tested using linear mixed-effects models and Cox regression models in longitudinal analyses. We also compared the effectiveness of these metrics with conventional metrics in predicting the clinical progression. Results: Although individuals in the ADNI cohort were older (74.68 [5.65] vs. 65.38 [4.66], p < 0.001) and had higher educational levels (16.3 [2.6] vs. 12.6 [2.8], p < 0.001) than those in the SILCODE cohort, similar loadings between resilience and its indicators were found within both models. BR and GR were mainly associated with age, women, and brain volume in both cohorts. Prediction models showed that higher CR and GR were related to better cognitive performance, and specifically, all types of resilience to CSF Aβ could predict longitudinal cognitive decline. Conclusion: Different phenotypes of resilience depending on cognition and brain volumes were associated with different factors. Such comprehensive resilience provided insight into the mechanisms of susceptibility for Alzheimer's disease (AD) at the individual level, and interindividual differences in resilience had the potential to predict the disease progression in CN people

Visiting Urban Green Space and Orientation to Nature Is Associated with Better Wellbeing during COVID-19 : International Journal of Environmental Research and Public Health

The COVID-19 pandemic has severely challenged mental health and wellbeing. However, research has consistently reinforced the value of spending time in green space for better health and wellbeing outcomes. Factors such as an individual’s nature orientation, used to describe one’s affinity to nature, may influence an individual’s green space visitation behaviour, and thus influence the wellbeing benefits gained. An online survey in Brisbane and Sydney, Australia (n = 2084), deployed during the COVID-19 pandemic (April 2021), explores if nature experiences and nature orientation are positively associated with personal wellbeing and if increased amounts of nature experiences are associated with improvement in wellbeing in the first year of the COVID-19 pandemic. We found that both yard and public green space visitation, as well as nature orientation scores, were correlated with high personal wellbeing scores, and individuals who spent more time in green space compared to the previous year also experienced a positive change in their health and wellbeing. Consistently, people with stronger nature orientations are also more likely to experience positive change. We also found that age was positively correlated to a perceived improvement in wellbeing over the year, and income was negatively correlated with a decreased change in wellbeing over the year, supporting other COVID-19 research that has shown that the effects of COVID-19 lifestyle changes were structurally unequal, with financially more established individuals experiencing better wellbeing. Such results highlight that spending time in nature and having high nature orientation are important for gaining those important health and wellbeing benefits and may provide a buffer for wellbeing during stressful periods of life that go beyond sociodemographic factors.Peer reviewe

Diabetes case finding in the emergency department, using HbA1c : an opportunity to improve diabetes detection, prevention, and care

Objective: We assessed the efficacy of routine glycated hemoglobin (HbA1c) testing to detect undiagnosed diabetes and prediabetes in an urban Australian public hospital emergency department (ED) located in an area of high diabetes prevalence. Methods: Over 6 weeks, all patients undergoing blood sampling in the ED had their random blood glucose measured. If ≥5.5 mmol/L (99 mg/dL), HbA1c was measured on the same sample. HbA1c levels ≥6.5% (48 mmol/mol) and 5.7-6.4% (39-46 mmol/mol) were diagnostic of diabetes and prediabetes, respectively. Hospital records were reviewed to identify patients with previously diagnosed diabetes. Results: Among 4580 presentations, 2652 had blood sampled of which 1267 samples had HbA1c measured. Of these, 487 (38.4%) had diabetes (either HbA1c≥6.5% or a prior diagnosis), and a further 347 (27.4%) had prediabetes. Among those with diabetes, 32.2% were previously undiagnosed. Conclusions: Routine HbA1c testing in the ED identifies a large number of people with undiagnosed diabetes and prediabetes, and provides an opportunity to improve their care

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

HTRA1 variant increases risk to neovascular age-related macular degeneration in Chinese population

AbstractAge-related macular degeneration (AMD) is a leading cause of irreversible visual impairment in the world. Advanced AMD can be divided into wet AMD (choroidal neovascularization) and dry AMD (geographic atrophy, GA). Drusen is characterized by deposits in the macula without visual loss and is an early AMD sign in the Caucasian population. rs11200638 in the promoter of HTRA1 has recently been shown to increases the risk for wet AMD in both Caucasian and Hong Kong Chinese populations. In order to replicate these results in a different cohort, we genotyped rs11200638 for 164 Chinese patients (90 wet AMD and 74 drusen) and 106 normal controls in a Han Mainland Chinese cohort. The genotypes were compared using chi square analysis for an additive allelic model. rs11200638 was significantly associated with wet AMD (p=5.00×10−12). Unlike in the Caucasian population, the risk allele of rs11200638 was not associated with drusen in our Chinese population. These findings confirm the association of HTRA1 with wet AMD

Beta distribution guided aspect-aware graph for aspect category sentiment analysis with affective knowledge

In this paper, we investigate the Aspect Category Sentiment Analysis (ACSA) task from a novel perspective by exploring a Beta Distribution guided aspect-aware graph construction based on external knowledge. That is, we are no longer entangled about how to laboriously search the sentiment clues for coarsegrained aspects from the context, but how to preferably find the words highly sentimentrelated to the aspects in the context and determine their importance based on the public knowledge base, so as to naturally learn the aspect-related contextual sentiment dependencies with these words in ACSA. To be specific, we first regard each aspect as a pivot to derive aspect-aware words that are highly related to the aspect from external affective commonsense knowledge. Then, we employ Beta Distribution to educe the aspect-aware weight, which reflects the importance to the aspect, for each aspect-aware word. Afterward, the aspect-aware words are served as the substitutes of the coarse-grained aspect to construct graphs for leveraging the aspectrelated contextual sentiment dependencies in ACSA. Experiments on 6 benchmark datasets show that our approach significantly outperforms the state-of-the-art baseline methods

Evaluation of the biological toxicity of fluorine in Antarctic krill

Antarctic krill is a potentially nutritious food source for humans, but fluorine (F) toxicity is a matter of concern. To evaluate the toxicity of F in Antarctic krill, 30 Wistar rats were divided into three groups with different dietary regimens: a control group, a krill treatment group (150 mg·kg−1 F), and a sodium fluoride (NaF) treatment group (150 mg·kg−1 F). After three months, F concentrations in feces, plasma, and bone were determined, and the degree of dental and skeletal fluorosis was assessed. The F concentrations in plasma and bone from the krill treatment group were 0.167 0±0.020 4 mg.L−1 and 2 709.8±301.9 mg·kg−1, respectively, compared with 0.043 8±0.005 5 mg·L−1 and 442.4±60.7 mg·kg−1, respectively, in samples from the control group. Concentrations of F in plasma and bone in the krill treatment group were higher than in the control group, but lower than in the NaF treatment group. The degree of dental fluorosis in the krill treatment group was moderate, compared with severe in the NaF treatment group and normal in the control group. The degree of skeletal fluorosis did not change significantly in any group. These results showed that the toxicity of F in Antarctic krill was lower than for an equivalent concentration of F in NaF, but it was toxic for rats consuming krill in large quantities. To conclude, we discuss possible reasons for the reduced toxicity of F in Antarctic krill. The present study provides a direct toxicological reference for the consideration of Antarctic krill for human consumption