3D Image quality improvement for optical projection tomography via point spread function modelling

Microbial Biotechnolog

Improving weakly supervised phrase grounding via visual representation contextualization with contrastive learning

Weakly supervised phrase grounding aims to map the phrases in an image caption to the objects appearing in the image under the supervision of image-caption correspondence. We observe that the current studies are insufficient to model the complicated interactions between the visual components (i.e., the visual regions) and between the visual and textual components (i.e., the phrases). Therefore, this paper presents a novel weakly supervised learning approach to phrase grounding in which we systematically model the visual contextualized representation with three modules: (1) object proposals pooling (OPP), (2) visual self-attention (VSA) and (3) visual-textual cross-modal attention (VTCA). OPP alleviates the suppression of the object proposals and benefits the visual representation in terms of trading off the richness of the visual components and the computational efficiency. VSA aims to capture the correlation among the object proposals and generate a representation of each proposal by incorporating the visual information of the others. To measure the cross-modal compatibility in terms of topics, we introduce the VTCA module to represent the visual topic corresponding to each textual component in a cross-modal common vector space. In the training process, we build a mixed contrastive loss function by considering both the cross-modal compatibility and the differences in the visual representations in the VSA module. Compared with the state-of-the-art methods, the proposed approach improves the performance by 3.88% points and 1.24% points on R@1, and by 2.23% points and 0.26% points on Pt_Acc, when trained on the MS COCO and Flickr30K Entities training sets, respectively. We have made our code available for follow-up research.Computer Systems, Imagery and Medi

Fine-grained label learning in object detection with weak supervision of captions

This paper addresses the task of fine-grained label learning in object detection with the weak supervision of auxiliary information attached to images. Most of the recent work focused on the label prediction for objects in the same category space as in training data under the fully-supervised learning framework and cannot be expanded to the learning of more fine-grained categories that have not been defined in training sets. In this paper, we propose a new weakly-supervised learning approach, called label inference curriculum network (LICN), to detecting objects and learning their fine-grained category labels based on supervision of captions via curriculum learning. First, we build a semantic mapping based on embedding techniques and a knowledge base to measure the correspondence between coarse labels and fine-grained label proposals; second, we introduce a label inference curriculum network, which ranks the order of training samples by the complexity of samples. We construct two datasets, namely FG-COCO and FGs-COCO, consisting of both coarse and fine-grained labels based on MS COCO and Visual Genome to train and test our approach. Experimental results demonstrate the effectiveness of our proposed LICN model, and LICN-E2C achieves an improvement of 1.7% mAP with 0.5:0.05:0.95 IoU compared with the LICN-C2E on the FG-sCOCO test dataset.Computer Systems, Imagery and Medi

The geographic scaling of biotic interactions

A central tenet of ecology and biogeography is that the broad outlines of species ranges are determined by climate, whereas the effects of biotic interactions are manifested at local scales. While the first proposition is supported by ample evidence, the second is still a matter of controversy. To address this question, we develop a mathematical model that predicts the spatial overlap, i.e. co-occurrence, between pairs of species subject to all possible types of interactions. We then identify the scale of resolution in which predicted range overlaps are lost. We found that co-occurrence arising from positive interactions, such as mutualism (+/+) and commensalism (+/0), are manifested across scales. Negative interactions, such as competition (-/-) and amensalism (-/0), generate checkerboard patterns of co-occurrence that are discernible at finer resolutions but that are lost and increasing scales of resolution. Scale dependence in consumer-resource interactions (+/-) depends on the strength of positive dependencies between species. If the net positive effect is greater than the net negative effect, then interactions scale up similarly to positive interactions. Our results challenge the widely held view that climate alone is sufficient to characterize species distributions at broad scales, but also demonstrate that the spatial signature of competition is unlikely to be discernible beyond local and regional scales. © 2013 The Authors.Peer Reviewe

Segmentation-driven optimization for iterative reconstruction in optical projection tomography: an exploration

Three-dimensional reconstruction of tomograms from optical projection microscopy is confronted with several drawbacks. In this paper we employ iterative reconstruction algorithms to avoid streak artefacts in the reconstruction and explore possible ways to optimize two parameters of the algorithms, i.e., iteration number and initialization, in order to improve the reconstruction performance. As benchmarks for direct reconstruction evaluation in optical projection tomography are absent, we consider the assessment through the performance of the segmentation on the 3D reconstruction. In our explorative experiments we use the zebrafish model system which is a typical specimen for use in optical projection tomography system; and as such frequently used. In this manner data can be easily obtained from which a benchmark set can be built. For the segmentation approach we apply a two-dimensional U-net convolutional neural network because it is recognized to have a good performance in biomedical image segmentation. In order to prevent the training from getting stuck in local minima, a novel learning rate schema is proposed. This optimization achieves a lower training loss during the training process, as compared to an optimal constant learning rate. Our experiments demonstrate that the approach to the benchmarking of iterative reconstruction via results of segmentation is very useful. It contributes an important tool to the development of computational tools for optical projection tomography.Computer Systems, Imagery and Medi

Appearance and vanishing of a spinal intradural arachnid cyst after multiple epidural corticosteroid injections. A spontaneously resolving cause of symptomatic lumbar stenosis

'Background: 'Spinal intradural arachnoid cyst is a rare lesion, sometimes acquired. 'Purpose: 'To describe the appearance and later spontaneous disappearence of a lumbar intradural arachnoid cyst, following perineural corticoid injections. 'Method: 'Review of the clinical data and imaging, with final spontaneous return to initial state. 'Discussion: 'Atypical intradural arachnoid cysts can be related to perineural injections and can cause symptoms of spinal stenosis. Its spontaneous vanishing is a very rare event, up to now unreported

Fuzzy Criteria in Multi-objective Feature Selection for Unsupervised Learning

Feature selection in which most informative variables are selected for model generation is an important step in pattern recognition. Here, one often tries to optimize multiple criteria such as discriminating power of the descriptor, performance of model and cardinality of a subset. In this paper we propose a fuzzy criterion in multi-objective unsupervised feature selection by applying the hybridized filter-wrapper approach (FC-MOFS). These formulations allow for an efficient way to pick features from a pool and to avoid misunderstanding of overlapping features via crisp clustered learning in a conventional multi-objective optimization procedure. Moreover, the optimization problem is solved by using non-dominated sorting genetic algorithm, type two (NSGA-II). The performance of the proposed approach is then examined on six benchmark datasets from multiple disciplines and different numbers of features. Systematic comparisons of the proposed method and representative non-fuzzified approaches are illustrated in this work. The experimental studies show a superior performance of the proposed approach in terms of accuracy and feasibility.Algorithms and the Foundations of Software technolog

Fluorescence and bright-field 3D image fusion based on sinogram unification for optical projection tomography

In order to preserve sufficient fluorescence intensity and improve the quality of fluorescence images in optical projection tomography (OPT) imaging, a feasible acquisition solution is to temporally formalize the fluorescence and bright-field imaging procedure as two consecutive phases. To be specific, fluorescence images are acquired first, in a full axial-view revolution, followed by the bright-field images. Due to the mechanical drift, this approach, however, may suffer from a deviation of center of rotation (COR) for the two imaging phases, resulting in irregular 3D image fusion, with which gene or protein activity may be located inaccurately. In this paper, we address this problem and consider it into a framework based on sinogram unification so as to precisely fuse 3D images from different channels for CORs between channels that are not coincident or if COR is not in the center of sinogram. The former case corresponds to the COR deviation above; while the latter one correlates with COR alignment, without which artefacts will be introduced in the reconstructed results. After sinogram unification, inverse radon transform can be implemented on each channel to reconstruct the 3D image. The fusion results are acquired by mapping the 3D images from different channels into a common space. Experimental results indicate that the proposed framework gains excellent performance in 3D image fusion from different channels. For the COR alignment, a new automated method based on interest point detection and included in sinogram unification, is presented. It outperforms traditional COR alignment approaches in combination of effectiveness and computational complexity.   Computer Systems, Imagery and Medi

Detecting virulent cells of Cryptococcus neoformans yeast: clustering experiments

Computer Systems, Imagery and Medi