scholarly journals Convolutional Neural Network-based Reconstruction for Positronium Annihilation Localization

2022 ◽  
Author(s):  
Jin Jegal ◽  
Dongwoo Jeong ◽  
Eun-Suk Seo ◽  
HyeoungWoo Park ◽  
Hongjoo Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Simulated Data ◽  
Tumor Localization ◽  
Data Sets ◽  
Silicon Photomultipliers ◽  
Spatial Error ◽  
Positron Emission ◽  
Oxide Crystal ◽  
Exotic Decay

Abstract A hermetic novel detector composed of 200 Bismuth germanium oxide crystal scintillators and 393 channel silicon photomultipliers has been developed for positronium (Ps) annihilation study. This compact 4π detector is capable of simultaneously detecting γ-ray decay in all directions, enabling not only the study of visible and invisible exotic decay processes but also tumor localization in positron emission tomography for small animals. In this study, we investigate the use of a convolutional neural network (CNN) for the localization of the Ps annihilation synonymous with tumor localization. The 2-γ decay systems of the Ps annihilation from the 22Na and 18F radioactive sources are simulated using GEANT4. The simulated data sets are preprocessed by applying energy cuts. The spatial error in the XY plane from CNN is compared to that from the classical centroiding, weighted k-means algorithm. The feasibility of the CNN-based Ps an-nihilation reconstruction with tumor localization is discussed.

scholarly journals Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 949
Author(s):  
Jiangyi Wang ◽  
Min Liu ◽  
Xinwu Zeng ◽  
Xiaoqiang Hua
Keyword(s):  
Neural Network ◽  
Signal Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Detection Method ◽  
Learning Algorithm ◽  
Simulated Data ◽  
Data Sets ◽  
Feature Maps ◽  
Simulated Data Sets

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

scholarly journals A convolutional neural network for estimating synaptic connectivity from spike trains

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daisuke Endo ◽  
Ryota Kobayashi ◽  
Ramon Bartolo ◽  
Bruno B. Averbeck ◽  
Yasuko Sugase-Miyamoto ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Generalized Linear Model ◽  
Simulated Data ◽  
Spike Trains ◽  
Synaptic Connectivity ◽  
Neuronal Circuits ◽  
Cortical Areas ◽  
Neuronal Spike Trains ◽  
Individual Dataset

AbstractThe recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a method of inferring this circuit connectivity from neuronal spike trains by applying the generalized linear model to cross-correlograms. Although the algorithm can do a good job of circuit reconstruction, the parameters need to be carefully tuned for each individual dataset. Here we present another method using a Convolutional Neural Network for Estimating synaptic Connectivity from spike trains. After adaptation to huge amounts of simulated data, this method robustly captures the specific feature of monosynaptic impact in a noisy cross-correlogram. There are no user-adjustable parameters. With this new method, we have constructed diagrams of neuronal circuits recorded in several cortical areas of monkeys.

scholarly journals AI INFLUENCE IN COVID-19 DETECTION

2021 ◽  
Vol 23 (07) ◽  
pp. 1116-1120
Author(s):  
Cijil Benny ◽  
Keyword(s):  
Neural Network ◽  
Statistical Analysis ◽  
Convolutional Neural Network ◽  
Case Reports ◽  
Ct Scans ◽  
Data Sets ◽  
X Rays ◽  
Medical Field ◽  
Primary Key ◽  
Made In

This paper is on analyzing the feasibility of AI studies and the involvement of AI in COVID interrelated treatments. In all, several procedures were reviewed and studied. It was on point. The best-analyzing methods on the studies were Susceptible Infected Recovered and Susceptible Exposed Infected Removed respectively. Whereas the implementation of AI is mostly done in X-rays and CT- Scans with the help of a Convolutional Neural Network. To accomplish the paper several data sets are used. They include medical and case reports, medical strategies, and persons respectively. Approaches are being done through shared statistical analysis based on these reports. Considerably the acceptance COVID is being shared and it is also reachable. Furthermore, much regulation is needed for handling this pandemic since it is a threat to global society. And many more discoveries shall be made in the medical field that uses AI as a primary key source.

scholarly journals Convolutional Neural Network (CNN) Based Three Dimensional Tumor Localization Using Single X-Ray Projection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 37026-37038 ◽  
Author(s):  
Ran Wei ◽  
Fugen Zhou ◽  
Bo Liu ◽  
Xiangzhi Bai ◽  
Dongshan Fu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Three Dimensional ◽  
Tumor Localization ◽  
X Ray ◽  
Ray Projection

PS02.070: DEEP NEURAL NETWORK TO PREDICT POOR PROGNOSTIC FACTORS IN PATIENTS WITH ESOPHAGEAL CANCER

2018 ◽  
Vol 31 (Supplement_1) ◽  
pp. 140-140
Author(s):  
Po-Kuei Hsu ◽  
Joe Yeh
Keyword(s):  
Neural Network ◽  
Positron Emission Tomography ◽  
Esophageal Cancer ◽  
Prognostic Factors ◽  
Convolutional Neural Network ◽  
Tumor Cells ◽  
Lymphovascular Invasion ◽  
Perineural Invasion ◽  
Emission Tomography ◽  
Positron Emission

Abstract Background Both lymphovascular invasion, which is characterized by penetration of tumor cells into the peritumoural vascular or lymphatic network, and perineural invasion, which is characterized by involvement of tumor cells surrounding nerve fibers, are considered as an important step for tumor spreading, and are known poor prognostic factors in esophageal cancer. However, the information of these histological features is unavailable until pathological examination of surgical resected specimens. We aim to predict the presence or absence of these factors by positron emission tomography images during staging workup. Methods The positron emission tomography images before treatment and pathological reports of 278 patients who underwent esophagectomy for squamous cell carcinoma were collected. Stepwise convolutional neural network was constructed to distinguish patient with either lymphovascular invasion or perineural invasion from those without. Results Randomly selected 248 patients were included in the testing set. Stepwise approach was used in training our custom neural network. The performance of fine-tuned neural network was tested in another independent 30 patients. The accuracy rate of predicting the presence or absence of either lymphovascular invasion or perineural invasion was 66.7% (20 of 30 were accurate). Conclusion Using pre-treatment positron emission tomography images alone to predict the presence of absence of poor prognostic histological factors, i.e. lymphovascular invasion or perineural invasion, with deep convolutional neural network is possible. The technique of deep learning may identify patients with poor prognosis and enable personalized medicine in esophageal cancer. Disclosure All authors have declared no conflicts of interest.

scholarly journals Kinetic Analysis of Drug–Target Interactions with PET for Characterization of Pharmacological Hysteresis

2013 ◽  
Vol 33 (5) ◽  
pp. 700-707 ◽  
Author(s):  
Cristian Salinas ◽  
David Weinzimmer ◽  
Graham Searle ◽  
David Labaree ◽  
Jim Ropchan ◽  
...  
Keyword(s):  
Simulated Data ◽  
Receptor Occupancy ◽  
Volume Of Distribution ◽  
Data Sets ◽  
Suitable Model ◽  
Dose Selection ◽  
Care Needs ◽  
Positron Emission

In vivo characterization of the brain pharmacokinetics of novel compounds provides important information for drug development decisions involving dose selection and the determination of administration regimes. In this context, the compound-target affinity is the key parameter to be estimated. However, if compounds exhibit a dynamic lag between plasma and target bound concentrations leading to pharmacological hysteresis, care needs to be taken to ensure the appropriate modeling approach is used so that the system is characterized correctly and that the resultant estimates of affinity are correct. This work focuses on characterizing different pharmacokinetic models that relate the plasma concentration to positron emission tomography outcomes measurements (e.g., volume of distribution and target occupancy) and their performance in estimating the true in vivo affinity. Measured (histamine H3 receptor antagonist—GSK189254) and simulated data sets enabled the investigation of different modeling approaches. An indirect pharmacokinetic-receptor occupancy model was identified as a suitable model for the calculation of affinity when a compound exhibits pharmacological hysteresis.

scholarly journals Identification of Natural Selection in Genomic Data with Deep Convolutional Neural Network

2021 ◽  
Author(s):  
Arnaud Nguembang Fadja ◽  
Fabrizio Riguzzi ◽  
Giorgio Bertorelle ◽  
Emiliano Trucchi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Natural Selection ◽  
Convolutional Neural Network ◽  
Simulated Data ◽  
Genomic Data ◽  
Relevant Information ◽  
Supervised Machine Learning ◽  
Selection Processes ◽  
Adaptive Processes

Abstract Background: With the increase in the size of genomic datasets describing variability in populations, extracting relevant information becomes increasingly useful as well as complex. Recently, computational methodologies such as Supervised Machine Learning and specifically Convolutional Neural Networks have been proposed to order to make inferences on demographic and adaptive processes using genomic data, Even though it was already shown to be powerful and efficient in different fields of investigation, Supervised Machine Learning has still to be explored as to unfold its enormous potential in evolutionary genomics. Results: The paper proposes a method based on Supervised Machine Learning for classifying genomic data, represented as windows of genomic sequences from a sample of individuals belonging to the same population. A Convolutional Neural Network is used to test whether a genomic window shows the signature of natural selection. Experiments performed on simulated data show that the proposed model can accurately predict neutral and selection processes on genomic data with more than 99% accuracy.

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