The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis

Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing with complex patterns, deep learning algorithms are ideal for image analysis challenges, particularly in the field of digital pathology. The variety of image analysis tasks in the context of deep learning includes classification (e.g., healthy vs. cancerous tissue), detection (e.g., lymphocytes and mitosis counting), and segmentation (e.g., nuclei and glands segmentation). The majority of recent machine learning methods in digital pathology have a pre- and/or post-processing stage which is integrated with a deep neural network. These stages, based on traditional image processing methods, are employed to make the subsequent classification, detection, or segmentation problem easier to solve. Several studies have shown how the integration of pre- and post-processing methods within a deep learning pipeline can further increase the model's performance when compared to the network by itself. The aim of this review is to provide an overview on the types of methods that are used within deep learning frameworks either to optimally prepare the input (pre-processing) or to improve the results of the network output (post-processing), focusing on digital pathology image analysis. Many of the techniques presented here, especially the post-processing methods, are not limited to digital pathology but can be extended to almost any image analysis field

Let's Twist Again: General Metrics of G(2) Holonomy from Gauged Supergravity

We construct all complete metrics of cohomogeneity one G(2) holonomy with S^3 x S^3 principal orbits from gauged supergravity. Our approach rests on a generalization of the twisting procedure used in this framework. It corresponds to a non-trivial embedding of the special Lagrangian three-cycle wrapped by the D6-branes in the lower dimensional supergravity. There are constraints that neatly reduce the general ansatz to a six functions one. Within this approach, the Hitchin system and the flop transformation are nicely realized in eight dimensional gauged supergravity.Comment: 31 pages, latex; v2: minor changes, references adde

Rotating membranes on G_2 manifolds, logarithmic anomalous dimensions and N=1 duality

We show that the $E-S \sim \log S$ behaviour found for long strings rotating on $AdS_5\times S^5$ may be reproduced by membranes rotating on $AdS_4\times S^7$ and on a warped $AdS_5$ M-theory solution. We go on to obtain rotating membrane configurations with the same $E-K \sim \log K$ relation on $G_2$ holonomy backgrounds that are dual to ${\mathcal{N}}=1$ gauge theories in four dimensions. We study membrane configurations on $G_2$ holonomy backgrounds systematically, finding various other Energy-Charge relations. We end with some comments about strings rotating on warped backgrounds.Comment: 1+44 pages. Latex. No figures. Minor corrections to make all membrane configurations consistent. One configuration is now noncompac

High affinity uptake of L-glutamate and γ-aminobutyric acid in Drosophila melanogaster

Preparations having properties resembling those of synaptosomes have been isolated from whole fly homogenates of Drosophila melanogaster using ficoll gradient floatation technique. These have been characterized by marker enzymes and electron microscopy and binding of muscarinic antagenist 3H Quinuclidinyl benzilate. An uptake system for neurotransmitter, a-Aminobutyric acid has been demonstrated in these preparations. A high affinity uptake system for L-glutamate has also been studied in these subcellular fractions. This uptake of glutamate is transport into an osmotically sensitive compartment and not due to binding of glutamate to membrane components. The transport of glutamate has an obligatory requirements for either sodium or potassium ions. Kinetic experiments show that two transport systems, with Km values 0.33×10-6M and 2.0×10-6, respectively, function in the accumulation of glutamate. ATP stimulates lower affinity transport of glutamate. Inhibition of glutamate uptake by L-aspartate but not by phenylalanine and tyrosine indicates that a common carrier mediates the transport of both glutamate and aspartate. β-N-oxalyl-L-β β-diamino propionic acid and kainic acid, both inhibitors of glutamate transport in mammalian brain preparations, strongly inhibited transport of glutamate in Drosophila preparations Comparison with uptake of a-aminobutyric acid and glutamate in isolated larval brain is presented to show that the synaptosome-like preparations we have isolated are rich in central nervous system derived structures, and presynaptic endings from neuromuscular junctions

Quality engineering of a traction alternator by robust design

Robust design is an engineering methodology for improving productivity during research and development so that high-quality products can be developed and produced quickly and at low cost. A large electrical company was developing traction alternators for a diesel electrical engine. Customer requirement was to obtain very high efficiency which, in turn, was influenced by several design parameters. The usual approach of the 'design-build-test' cycle was considered time-consuming and costly; it used to take anywhere from 4 months to 1 year before finalizing the product design parameters as it involved physical assembly and also testing. Instead, the authors used Taguchi's parameter design approach. This approach took about 8 weeks to arrive at optimum design parameter values; clearly demonstrating the cutting edge of this methodology over the traditional design-build-test approach. The prototype built and tested accordingly gave satisfactory overall performance, meeting and even exceeding customer requirements

Cost-Effective and Non-Invasive Automated Benign & Malignant Thyroid Lesion Classification in 3D Contrast-Enhanced Ultrasound Using Combination of Wavelets and Textures: A Class of ThyroScan™ Algorithms

Ultrasound has great potential to aid in the differential diagnosis of malignant and benign thyroid lesions, but interpretative pitfalls exist and the accuracy is still poor. To overcome these difficulties, we developed and analyzed a range of knowledge representation techniques, which are a class of ThyroScan™ algorithms from Global Biomedical Technologies Inc., California, USA, for automatic classification of benign and malignant thyroid lesions. The analysis is based on data obtained from twenty nodules (ten benign and ten malignant) taken from 3D contrast-enhanced ultrasound images. Fine needle aspiration biopsy and histology confirmed malignancy. Discrete Wavelet Transform (DWT) and texture algorithms are used to extract relevant features from the thyroid images. The resulting feature vectors are fed to three different classifiers: K-Nearest Neighbor (K-NN), Probabilistic Neural Network (PNN), and Decision Tree (DeTr). The performance of these classifiers is compared using Receiver Operating Characteristic (ROC) curves. Our results show that combination of DWT and texture features coupled with K-NN resulted in good performance measures with the area of under the ROC curve of 0.987, a classification accuracy of 98.9%, a sensitivity of 98%, and a specificity of 99.8%. Finally, we have proposed a novel integrated index called Thyroid Malignancy Index (TMI), which is made up of texture features, to diagnose benign or malignant nodules using just one index. We hope that this TMI will help clinicians in a more objective detection of benign and malignant thyroid lesions

PDRL: Multi-Agent based Reinforcement Learning for Predictive Monitoring

Reinforcement learning has been increasingly applied in monitoring applications because of its ability to learn from previous experiences and can make adaptive decisions. However, existing machine learning-based health monitoring applications are mostly supervised learning algorithms, trained on labels and they cannot make adaptive decisions in an uncertain complex environment. This study proposes a novel and generic system, predictive deep reinforcement learning (PDRL) with multiple RL agents in a time series forecasting environment. The proposed generic framework accommodates virtual Deep Q Network (DQN) agents to monitor predicted future states of a complex environment with a well-defined reward policy so that the agent learns existing knowledge while maximizing their rewards. In the evaluation process of the proposed framework, three DRL agents were deployed to monitor a subject's future heart rate, respiration, and temperature predicted using a BiLSTM model. With each iteration, the three agents were able to learn the associated patterns and their cumulative rewards gradually increased. It outperformed the baseline models for all three monitoring agents. The proposed PDRL framework is able to achieve state-of-the-art performance in the time series forecasting process. The proposed DRL agents and deep learning model in the PDRL framework are customized to implement the transfer learning in other forecasting applications like traffic and weather and monitor their states. The PDRL framework is able to learn the future states of the traffic and weather forecasting and the cumulative rewards are gradually increasing over each episode.Comment: This work has been submitted to the Springer for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl