Convolutional deep belief network with feature encoding for classification of neuroblastoma histological images

© 2018 Journal of Pathology Informatics. Background: Neuroblastoma is the most common extracranial solid tumor in children younger than 5 years old. Optimal management of neuroblastic tumors depends on many factors including histopathological classification. The gold standard for classification of neuroblastoma histological images is visual microscopic assessment. In this study, we propose and evaluate a deep learning approach to classify high-resolution digital images of neuroblastoma histology into five different classes determined by the Shimada classification. Subjects and Methods: We apply a combination of convolutional deep belief network (CDBN) with feature encoding algorithm that automatically classifies digital images of neuroblastoma histology into five different classes. We design a three-layer CDBN to extract high-level features from neuroblastoma histological images and combine with a feature encoding model to extract features that are highly discriminative in the classification task. The extracted features are classified into five different classes using a support vector machine classifier. Data: We constructed a dataset of 1043 neuroblastoma histological images derived from Aperio scanner from 125 patients representing different classes of neuroblastoma tumors. Results: The weighted average F-measure of 86.01% was obtained from the selected high-level features, outperforming state-of-the-art methods. Conclusion: The proposed computer-aided classification system, which uses the combination of deep architecture and feature encoding to learn high-level features, is highly effective in the classification of neuroblastoma histological images

Bessel beam illumination reduces random and systematic errors in quantitative functional studies using light-sheet microscopy

Light-sheet microscopy (LSM), in combination with intrinsically transparent zebrafish larvae, is a choice method to observe brain function with high frame rates at cellular resolution. Inherently to LSM, however, residual opaque objects cause stripe artifacts, which obscure features of interest and, during functional imaging, modulate fluorescence variations related to neuronal activity. Here, we report how Bessel beams reduce streaking artifacts and produce high-fidelity quantitative data demonstrating a fivefold increase in sensitivity to calcium transients and a 20 fold increase in accuracy in the detection of activity correlations in functional imaging. Furthermore, using principal component analysis, we show that measurements obtained with Bessel beams are clean enough to reveal in one-shot experiments correlations that can not be averaged over trials after stimuli as is the case when studying spontaneous activity. Our results not only demonstrate the contamination of data by systematic and random errors through conventional Gaussian illumination and but,furthermore, quantify the increase in fidelity of such data when using Bessel beams

Study of Reproductive Number in SIR-SIS Model

Abstract The thresholds for mathematical epidemiology models specify the critical conditions for an epidemic to grow or die out. The reproductive number can provide significant insight into the transmission dynamics of a disease and can guide strategies to control its spread. We define the mean number of contacts, the mean duration of infection, and the mean transmission probability appropriately for certain epidemiological models, and construct a simplified formulation of the reproductive number as the product of these quantities. When the spread of the epidemic depends strongly upon the heterogeneity, and the expressions for the reproductive number become correspondingly more complex. In this article we formulate a model with different heterogeneous structures and demonstrate how to define the mean quantities for an explicit expression for the reproductive number. We derive an explicit formula for the reproductive number employing the spectral radius of the next generation operator

A combined convolutional and recurrent neural network for enhanced glaucoma detection.

Glaucoma, a leading cause of blindness, is a multifaceted disease with several patho-physiological features manifesting in single fundus images (e.g., optic nerve cupping) as well as fundus videos (e.g., vascular pulsatility index). Current convolutional neural networks (CNNs) developed to detect glaucoma are all based on spatial features embedded in an image. We developed a combined CNN and recurrent neural network (RNN) that not only extracts the spatial features in a fundus image but also the temporal features embedded in a fundus video (i.e., sequential images). A total of 1810 fundus images and 295 fundus videos were used to train a CNN and a combined CNN and Long Short-Term Memory RNN. The combined CNN/RNN model reached an average F-measure of 96.2% in separating glaucoma from healthy eyes. In contrast, the base CNN model reached an average F-measure of only 79.2%. This proof-of-concept study demonstrates that extracting spatial and temporal features from fundus videos using a combined CNN and RNN, can markedly enhance the accuracy of glaucoma detection

Spanning trees of 2-complexes from diagram groups over the construction of semigroup presentation of integers using lifting method

For any given semigroup presentation we may obtain the fundamental group. In this paper we will determine spanning trees for the 2-complexes of the fundamental groups obtained from the union of two semigroup presentations with finite different initial generators using lifting method. The spanning trees will be systematically selected by using lifting method according to the length of words. Also the general formula for all lifts of spanning trees and the number of edges in the spanning trees will be computed

Classification of Neuroblastoma Histopathological Images Using Machine Learning

Neuroblastoma is the most common cancer in young children accounting for over 15% of deaths in children due to cancer. Identification of the class of neuroblastoma is dependent on histopathological classification performed by pathologists which are considered the gold standard. However, due to the heterogeneous nature of neuroblast tumours, the human eye can miss critical visual features in histopathology. Hence, the use of computer-based models can assist pathologists in classification through mathematical analysis. There is no publicly available dataset containing neuroblastoma histopathological images. So, this study uses dataset gathered from The Tumour Bank at Kids Research at The Children’s Hospital at Westmead, which has been used in previous research. Previous work on this dataset has shown maximum accuracy of 84%. One main issue that previous research fails to address is the class imbalance problem that exists in the dataset as one class represents over 50% of the samples. This study explores a range of feature extraction and data undersampling and over-sampling techniques to improve classification accuracy. Using these methods, this study was able to achieve accuracy of over 90% in the dataset. Moreover, significant improvements observed in this study were in the minority classes where previous work failed to achieve high level of classification accuracy. In doing so, this study shows importance of effective management of available data for any application of machine learning

Computer Aided Classification of Neuroblastoma Histological Images Using Scale Invariant Feature Transform with Feature Encoding

Neuroblastoma is the most common extracranial solid malignancy in early childhood. Optimal management of neuroblastoma depends on many factors, including histopathological classification. Although histopathology study is considered the gold standard for classification of neuroblastoma histological images, computers can help to extract many more features some of which may not be recognizable by human eyes. This paper, proposes a combination of Scale Invariant Feature Transform with feature encoding algorithm to extract highly discriminative features. Then, distinctive image features are classified by Support Vector Machine classifier into five clinically relevant classes. The advantage of our model is extracting features which are more robust to scale variation compared to the Patched Completed Local Binary Pattern and Completed Local Binary Pattern methods. We gathered a database of 1043 histologic images of neuroblastic tumours classified into five subtypes. Our approach identified features that outperformed the state-of-the-art on both our neuroblastoma dataset and a benchmark breast cancer dataset. Our method shows promise for classification of neuroblastoma histological images

Spanning Trees of 2-Complexes from Diagram Groups over the Construction of Semigroup Presentation of Integers using Lifting Method

ABSTRACT For any given semigroup presentation we may obtain the fundamental group. In this paper we will determine spanning trees for the 2-complexes of the fundamental groups obtained from the union of two semigroup presentations with finite different initial generators using lifting method. The spanning trees will be systematically selected by using lifting method according to the length of words. Also the general formula for all lifts of spanning trees and the number of edges in the spanning trees will be computed

Multi-material additive manufacturing of low sintering temperature Bi2Mo2O9 ceramics with Ag floating electrodes by selective laser burnout

Additive manufacturing (AM) of co-fired low temperature ceramics offers a unique route for fabrication of novel 3D radio frequency (RF) and microwave communication components, embedded electronics and sensors. This paper describes the first-ever direct 3D printing of low temperature co-fired ceramics/floating electrode 3D structures. Slurry-based AM and selective laser burnout (SLB) were used to fabricate bulk dielectric, Bi2Mo2O9 (BMO, sintering temperature = 620–650°C, εr = 38) with silver (Ag) internal floating electrodes. A printable BMO slurry was developed and the SLB optimised to improve edge definition and burn out the binder without damaging the ceramic. The SLB increased the green strength needed for shape retention, produced crack-free parts and prevented Ag leaching into the ceramic during co-firing. The green parts were sintered after SLB in a conventional furnace at 645°C for 4 h and achieved 94.5% density, compressive strength of 4097 MPa, a relative permittivity (εr) of 33.8 and a loss tangent (tan δ) of 0.0004 (8 GHz) for BMO. The feasibility of using SLB followed by a post-printing sintering step to create BMO/Ag 3D structures was thus demonstrated

Analysis of microRNA signatures using size-coded ligation-mediated PCR

The expression pattern and regulatory functions of microRNAs (miRNAs) are intensively investigated in various tissues, cell types and disorders. Differential miRNA expression signatures have been revealed in healthy and unhealthy tissues using high-throughput profiling methods. For further analyses of miRNA signatures in biological samples, we describe here a simple and efficient method to detect multiple miRNAs simultaneously in total RNA. The size-coded ligation-mediated polymerase chain reaction (SL-PCR) method is based on size-coded DNA probe hybridization in solution, followed-by ligation, PCR amplification and gel fractionation. The new method shows quantitative and specific detection of miRNAs. We profiled miRNAs of the let-7 family in a number of organisms, tissues and cell types and the results correspond with their incidence in the genome and reported expression levels. Finally, SL-PCR detected let-7 expression changes in human embryonic stem cells as they differentiate to neuron and also in young and aged mice brain and bone marrow. We conclude that the method can efficiently reveal miRNA signatures in a range of biological samples