Scalable motion compensated scan-rate up-conversion

A method for scalable motion compensated up-conversion of video sequences is proposed. By computing suitable objective quality estimates, (which show a high correlation with subjective quality scores), the method allows to predict the resources needed to up-convert a given input video sequence achieving a certain visual quality of the output video sequence. This method permits to dynamically change the resource utilisation in a way which is optimal for the system, e.g. for a programmable platform for media processing

Fast detection of novel problematic patterns based on dictionary learning and prediction of their lithographic difficulty

Assessing pattern printability in new large layouts faces important challenges of runtime and false detection. Lithographic simulation tools and classification techniques do not scale well. We propose a fast pattern detection method by learning an overcomplete basis representing each reference pattern. A pattern from a new design is detected “novel” if its reconstruction error, when coded in the learned basis, is large. We show high speedup (1000x) compared to nearest neighbor search. A new boundary detection technique selects the minimal set of the novel patterns to predict problematic patterns; 14.93% of the novel patterns suffice to predict ORC hotspots, while 53.77% are needed using traditional methods

Image-based computational quantification and visualization of genetic alterations and tumour heterogeneity

Recent large-scale genome analyses of human tissue samples have uncovered a high degree of genetic alterations and tumour heterogeneity in most tumour entities, independent of morphological phenotypes and histopathological characteristics. Assessment of genetic copy-number variation (CNV) and tumour heterogeneity by fluorescence in situ hybridization (ISH) provides additional tissue morphology at single-cell resolution, but it is labour intensive with limited throughput and high inter-observer variability. We present an integrative method combining bright-field dual-colour chromogenic and silver ISH assays with an image-based computational workflow (ISHProfiler), for accurate detection of molecular signals, high-throughput evaluation of CNV, expressive visualization of multi-level heterogeneity (cellular, inter- and intra-tumour heterogeneity), and objective quantification of heterogeneous genetic deletions (PTEN) and amplifications (19q12, HER2) in diverse human tumours (prostate, endometrial, ovarian and gastric), using various tissue sizes and different scanners, with unprecedented throughput and reproducibility

Privacy preserving distributed learning classifiers-Sequential learning with small sets of data

Background: Artificial intelligence (AI) typically requires a significant amount of high-quality data to build reliable models, where gathering enough data within a single institution can be particularly challenging. In this study we investigated the impact of using sequential learning to exploit very small, siloed sets of clinical and imaging data to train AI models. Furthermore, we evaluated the capacity of such models to achieve equivalent performance when compared to models trained with the same data over a single centralized database. Methods: We propose a privacy preserving distributed learning framework, learning sequentially from each dataset. The framework is applied to three machine learning algorithms: Logistic Regression, Support Vector Machines (SVM), and Perceptron. The models were evaluated using four open-source datasets (Breast cancer, Indian liver, NSCLC-Radiomics dataset, and Stage III NSCLC). Findings: The proposed framework ensured a comparable predictive performance against a centralized learning approach. Pairwise DeLong tests showed no significant difference between the compared pairs for each dataset. Interpretation: Distributed learning contributes to preserve medical data privacy. We foresee this technology will increase the number of collaborative opportunities to develop robust AI, becoming the default solution in scenarios where collecting enough data from a single reliable source is logistically impossible. Distributed sequential learning provides privacy persevering means for institutions with small but clinically valuable datasets to collaboratively train predictive AI while preserving the privacy of their patients. Such models perform similarly to models that are built on a larger central dataset

Erratum: On the role of artificial intelligence in medical imaging of COVID-19 (Patterns (2021) 2(6), (S2666389921000957), (10.1016/j.patter.2021.100269))

The corrected full text is available on Brunel University Reasearch Archive at: https://bura.brunel.ac.uk/handle/2438/26331.Copyright © 2021 The Author(s). (Patterns 2, 100269-1–100269-18; June 11, 2021). Due to an error in production, the references for this article were incorrectly numbered upon publication of the typeset paper, May 26, 2021. Since this is a meta-analysis, incorrect numbering of references may have caused misinterpretation of the conclusions. The corrected version of the article is available online as of June 5, 2021, and we sincerely apologize to our readers for any confusion that may have resulted from this error

Inequalities in the burden of non-communicable diseases across European countries: a systematic analysis of the Global Burden of Disease 2019 study

Abstract Background Although overall health status in the last decades improved, health inequalities due to non-communicable diseases (NCDs) persist between and within European countries. There is a lack of studies giving insights into health inequalities related to NCDs in the European Economic Area (EEA) countries. Therefore, the aim of the present study was to quantify health inequalities in age-standardized disability adjusted life years (DALY) rates for NCDs overall and 12 specific NCDs across 30 EEA countries between 1990 and 2019. Also, this study aimed to determine trends in health inequalities and to identify those NCDs where the inequalities were the highest. Methods DALY rate ratios were calculated to determine and compare inequalities between the 30 EEA countries, by sex, and across time. Annual rate of change was used to determine the differences in DALY rate between 1990 and 2019 for males and females. The Gini Coefficient (GC) was used to measure the DALY rate inequalities across countries, and the Slope Index of Inequality (SII) to estimate the average absolute difference in DALY rate across countries. Results Between 1990 and 2019, there was an overall declining trend in DALY rate, with larger declines among females compared to males. Among EEA countries, in 2019 the highest NCD DALY rate for both sexes were observed for Bulgaria. For the whole period, the highest DALY rate ratios were identified for digestive diseases, diabetes and kidney diseases, substance use disorders, cardiovascular diseases (CVD), and chronic respiratory diseases – representing the highest inequality between countries. In 2019, the highest DALY rate ratio was found between Bulgaria and Iceland for males. GC and SII indicated that the highest inequalities were due to CVD for most of the study period – however, overall levels of inequality were low. Conclusions The inequality in level 1 NCDs DALYs rate is relatively low among all the countries. CVDs, digestive diseases, diabetes and kidney diseases, substance use disorders, and chronic respiratory diseases are the NCDs that exhibit higher levels of inequality across countries in the EEA. This might be mitigated by applying tailored preventive measures and enabling healthcare access