Convolutional Neural Networks

Venkatesan, Ragav; Li, Baoxin

doi:10.4324/9781315154282-4

Cited by 21 publications

(16 citation statements)

References 1 publication

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“…By using of a fully connected algorithm, a network can conquer the overfitting problem. The CNN algorithm examines the clinical field so that, every neuron in a human cell appears like the visual cortex (Venkatesan and Li 2017 ). In ligand-protein interaction, many researchers utilized CNN model for predicting affinity in protein-ligand (LeCun et al.…”

Section: Machine Learning Methods To Drug Discoverymentioning

confidence: 99%

Machine Learning in Drug Discovery: A Review

Dara¹,

Dhamercherla²,

et al. 2021

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This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.

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Section: Machine Learning Methods To Drug Discoverymentioning

confidence: 99%

Machine Learning in Drug Discovery: A Review

Dara¹,

Dhamercherla²,

et al. 2021

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“…A convolutional neural network is a select type of multilayer neural networks [ 21 ]. It has high capacity to recognize existing patterns in images, where little computational effort is required to treat pixels.…”

Section: Proposed Methods For Detection Location and Classification Of Rail Surface Defectsmentioning

confidence: 99%

Detection and Classification System for Rail Surface Defects Based on Eddy Current

Alvarenga

Carvalho

Honório

et al. 2021

Sensors

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The prospect of growth of a railway system impacts both the network size and its occupation. Due to the overloaded infrastructure, it is necessary to increase reliability by adopting fast maintenance services to reach economic and security conditions. In this context, one major problem is the excessive friction caused by the wheels. This contingency may cause ruptures with severe consequences. While eddy’s current approaches are adequate to detect superficial damages in metal structures, there are still open challenges concerning automatic identification of rail defects. Herein, we propose an embedded system for online detection and location of rails defects based on eddy current. Moreover, we propose a new method to interpret eddy current signals by analyzing their wavelet transforms through a convolutional neural network. With this approach, the embedded system locates and classifies different types of anomalies, enabling an optimization of the railway maintenance plan. Field tests were performed, in which the rail anomalies were grouped in three classes: squids, weld and joints. The results showed a classification efficiency of ~98%, surpassing the most commonly used methods found in the literature.

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“…Again, in analogy to biological visual systems, the receptive fields of neurons in the higher levels generally increase in size, so that neurons in the highest few layers can detect a feature anywhere in the visual field. The VGGNet is convolutional ( Simonyan and Zisserman, 2015 ; Venkatesan and Baoxin, 2017 ; Chougrad et al, 2018 ), in that it replicates, with the same weights, each feature at every position in the input image. While generally not considered biologically plausible, this is a useful computational shortcut that allows significant reduction of both the computational requirements and the amount of necessary training data.…”

Section: Methodsmentioning

confidence: 99%

“…For this project, PVMs were generated from the whole-breast mammograms available in the Digital Database for Screening Mammography (DDSM) database ( Heath et al, 2001 ; Venkatesan and Baoxin, 2017 ). Each mammogram belonged to one of the following four radiologically determined categories: “normal,” “benign without callback” (which were so obviously benign that the patient did not have to be re-examined), “benign” (ambiguous enough to necessitate patient callback, but eventually determined to be benign), and “cancer.”…”

Section: Methodsmentioning

confidence: 99%

Deep Synthesis of Realistic Medical Images: A Novel Tool in Clinical Research and Training

Bart¹,

Hegdé²

2018

Front. Neuroinform.

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Making clinical decisions based on medical images is fundamentally an exercise in statistical decision-making. This is because in this case, the decision-maker must distinguish between image features that are clinically diagnostic (i.e., signal) from a large amount of non-diagnostic features. (i.e., noise). To perform this task, the decision-maker must have learned the underlying statistical distributions of the signal and noise to begin with. The same is true for machine learning algorithms that perform a given diagnostic task. In order to train and test human experts or expert machine systems in any diagnostic or analytical task, it is advisable to use large sets of images, so as to capture the underlying statistical distributions adequately. Large numbers of images are also useful in clinical and scientific research about the underlying diagnostic process, which remains poorly understood. Unfortunately, it is often difficult to obtain medical images of given specific descriptions in sufficiently large numbers. This represents a significant barrier to progress in the arenas of clinical care, education, and research. Here we describe a novel methodology that helps overcome this barrier. This method leverages the burgeoning technologies of deep learning (DL) and deep synthesis (DS) to synthesize medical images de novo. We provide a proof-of-principle of this approach using mammograms as an illustrative case. During the initial, prerequisite DL phase of the study, we trained a publicly available deep learning neural network (DNN), using open-sourced, radiologically vetted mammograms as labeled examples. During the subsequent DS phase of the study, the fully trained DNN was made to synthesize, de novo, images that capture the image statistics of a given input image. The resulting images indicated that our DNN was able to faithfully capture the image statistics of visually diverse sets of mammograms. We also briefly outline rigorous psychophysical testing methods to measure the extent to which synthesized mammography were sufficiently alike their original counterparts to human experts. These tests reveal that mammography experts fail to distinguish synthesized mammograms from their original counterparts at a statistically significant level, suggesting that the synthesized images were sufficiently realistic. Taken together, these results demonstrate that deep synthesis has the potential to be impactful in all fields in which medical images play a key role, most notably in radiology and pathology.

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Convolutional Neural Networks

Cited by 21 publications

References 1 publication

Machine Learning in Drug Discovery: A Review

Machine Learning in Drug Discovery: A Review

Detection and Classification System for Rail Surface Defects Based on Eddy Current

Deep Synthesis of Realistic Medical Images: A Novel Tool in Clinical Research and Training

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