Learning image quality assessment by reinforcing task amenable data selection

In this paper, we consider a type of image quality assessment as a task-specific measurement, which can be used to select images that are more amenable to a given target task, such as image classification or segmentation. We propose to train simultaneously two neural networks for image selection and a target task using reinforcement learning. A controller network learns an image selection policy by maximising an accumulated reward based on the target task performance on the controller-selected validation set, whilst the target task predictor is optimised using the training set. The trained controller is therefore able to reject those images that lead to poor accuracy in the target task. In this work, we show that the controller-predicted image quality can be significantly different from the task-specific image quality labels that are manually defined by humans. Furthermore, we demonstrate that it is possible to learn effective image quality assessment without using a ``clean'' validation set, thereby avoiding the requirement for human labelling of images with respect to their amenability for the task. Using $6712$, labelled and segmented, clinical ultrasound images from $259$ patients, experimental results on holdout data show that the proposed image quality assessment achieved a mean classification accuracy of $0.94\pm0.01$ and a mean segmentation Dice of $0.89\pm0.02$, by discarding $5\%$ and $15\%$ of the acquired images, respectively. The significantly improved performance was observed for both tested tasks, compared with the respective $0.90\pm0.01$ and $0.82\pm0.02$ from networks without considering task amenability. This enables image quality feedback during real-time ultrasound acquisition among many other medical imaging applications

Adaptable image quality assessment using meta-reinforcement learning of task amenability

The performance of many medical image analysis tasks are strongly associated with image data quality. When developing modern deep learning algorithms, rather than relying on subjective (human-based) image quality assessment (IQA), task amenability potentially provides an objective measure of task-specific image quality. To predict task amenability, an IQA agent is trained using reinforcement learning (RL) with a simultaneously optimised task predictor, such as a classification or segmentation neural network. In this work, we develop transfer learning or adaptation strategies to increase the adaptability of both the IQA agent and the task predictor so that they are less dependent on high-quality, expert-labelled training data. The proposed transfer learning strategy re-formulates the original RL problem for task amenability in a meta-reinforcement learning (meta-RL) framework. The resulting algorithm facilitates efficient adaptation of the agent to different definitions of image quality, each with its own Markov decision process environment including different images, labels and an adaptable task predictor. Our work demonstrates that the IQA agents pre-trained on non-expert task labels can be adapted to predict task amenability as defined by expert task labels, using only a small set of expert labels. Using 6644 clinical ultrasound images from 249 prostate cancer patients, our results for image classification and segmentation tasks show that the proposed IQA method can be adapted using data with as few as respective 19.7 % % and 29.6 % % expert-reviewed consensus labels and still achieve comparable IQA and task performance, which would otherwise require a training dataset with 100 % % expert labels

Galectin-1 as a potential cancer target

Galectins are a family of structurally related carbohydrate-binding proteins, which are defined by their affinity for poly-N-acetyllactosamine-enriched glycoconjugates and sequence similarities in the carbohydrate recognition domain. Galectin-1, a member of this family, contributes to different events associated with cancer biology, including tumour transformation, cell cycle regulation, apoptosis, cell adhesion, migration and inflammation. In addition, recent evidence indicates that galectin-1 contributes to tumour evasion of immune responses. Given the increased interest of tumour biologists and clinical oncologists in this field and the potential use of galectins as novel targets for anticancer drugs, we summarise here recent advances about the role of galectin-1 in different events of tumour growth and metastasis

Fluoroquinolone resistance during 2000–2005 : An observational study

<p>Abstract</p> <p>Background</p> <p>Moxifloxacin is a respiratory fluoroquinolone with a community acquired pneumonia indication. Unlike other fluoroquinolones used in our healthcare system, moxifloxacin's urinary excretion is low and thus we hypothesized that increased use of moxifloxacin is associated with an increase in fluoroquinolone resistance amongst gram negative uropathogens.</p> <p>Methods</p> <p>All antibiograms for Gram negative bacteria were obtained for 2000 to 2005. The defined daily dose (DDD) for each fluoroquinolone was computed according to World Health Organization criteria. To account for fluctuation in patient volume, DDD/1000 bed days was computed for each year of study. Association between DDD/1000 bed days for each fluoroquinolone and the susceptibility of Gram negative bacteria to ciprofloxacin was assessed using Pearson's Correlation Coefficient, r.</p> <p>Results</p> <p>During the study period, there were 48,261 antibiograms, 347,931 DDD of fluoroquinolones, and 1,943,338 bed days. Use of fluoroquinolones among inpatients decreased from 237.2 DDD/1000 bed days in 2000 to 115.2 DDD/1000 bed days in 2005. With the exception of <it>Enterobacter aerogenes</it>, moxifloxacin use was negatively correlated with sensitivity among all 13 Gram negative species evaluated (r = -0.07 to -0.97). When the sensitivities of all Gram negative organisms were aggregated, all fluoroquinolones except moxifloxacin were associated with increased sensitivity (r = 0.486 to 1.000) while moxifloxacin was associated with decreased sensitivity (r = -0.464).</p> <p>Conclusion</p> <p>Moxifloxacin, while indicated for empiric treatment of community acquired pneumonia, may have important negative influence on local antibiotic sensitivities amongst Gram negative organisms. This effect was not shared by other commonly used members of the fluoroquinolone class.</p

Estrogen receptor-β: why may it influence clinical outcome in estrogen receptor-α positive breast cancer?

In the previous issue of the journal, Lin and coworkers present data demonstrate that increased expression of estrogen receptor (ER)-β in ER-α-positive breast cancer cells antagonizes a defined group of ER-α/estrogen stimulated genes that are involved in cell cycle regulation and DNA replication. Similar expression patterns for these genes were found human ER-α positive breast tumors expressing higher levels or ER-β, and this correlated with better clinical outcome. The implications for these data, which suggest that ER-β is a positive actor and diagnostic marker for therapeutic outcome, are discussed

The Populus holobiont: dissecting the effects of plant niches and genotype on the microbiome

Background: Microorganisms serve important functions within numerous eukaryotic host organisms. An understanding of the variation in the plant niche-level microbiome, from rhizosphere soils to plant canopies, is imperative to gain a better understanding of how both the structural and functional processes of microbiomes impact the health of the overall plant holobiome. Using Populus trees as a model ecosystem, we characterized the archaeal/bacterial and fungal microbiome across 30 different tissue-level niches within replicated Populus deltoides and hybrid Populus trichocarpa × deltoides individuals using 16S and ITS2 rRNA gene analyses. Results: Our analyses indicate that archaeal/bacterial and fungal microbiomes varied primarily across broader plant habitat classes (leaves, stems, roots, soils) regardless of plant genotype, except for fungal communities within leaf niches, which were greatly impacted by the host genotype. Differences between tree genotypes are evident in the elevated presence of two potential fungal pathogens, Marssonina brunnea and Septoria sp., on hybrid P. trichocarpa × deltoides trees which may in turn be contributing to divergence in overall microbiome composition. Archaeal/bacterial diversity increased from leaves, to stem, to root, and to soil habitats, whereas fungal diversity was the greatest in stems and soils. Conclusions: This study provides a holistic understanding of microbiome structure within a bioenergy relevant plant host, one of the most complete niche-level analyses of any plant. As such, it constitutes a detailed atlas or map for further hypothesis testing on the significance of individual microbial taxa within specific niches and habitats of Populus and a baseline for comparisons to other plant species

The Forward-Discount Puzzle in Central and Eastern Europe

This paper adds to evidence that the forward-discount puzzle is at least partly explained as a compensation for taking crash-risk. A number of Central and Eastern European exchange rates are compared. A Hidden Markov Model is used to identify two regimes for most of the exchange rates. These two regimes can be characterised as being either periods of stability or periods of instability. The level of international risk aversion and changes in US interest rates affect the probability of switching from one regime to the other. This model is then used to assess the way that these two factors affect the probability of a currency crisis. While the Czech Republic, Hungary and Bulgaria are very sensitive to international financial conditions, Poland and Romania are relatively immune. JEL classifications: C24, F31, F32; Key words: Exchange rates, uncovered interest parity, foreign exchange risk discount, hidden-Markov model, carry-trad

The Road Less Traveled: Regulation of Leukocyte Migration Across Vascular and Lymphatic Endothelium by Galectins

Leukocyte entry from the blood into inflamed tissues, exit into the lymphatics, and migration to regional lymph nodes are all crucial processes for mounting an effective adaptive immune response. Leukocytes must cross two endothelial cell layers, the vascular and the lymphatic endothelial cell layers, during the journey from the blood to the lymph node. The proteins and cellular interactions which regulate leukocyte migration across the vascular endothelium are well studied; however, little is known about the factors that regulate leukocyte migration across the lymphatic endothelium. Here, we will summarize evidence for a role for galectins, a family of carbohydrate-binding proteins, in regulating leukocyte migration across the vascular endothelium and propose that galectins are also involved in leukocyte migration across the lymphatic endothelium