scholarly journals InRFNet: Involution Receptive Field Network for COVID-19 Diagnosis

2022 ◽  
Vol 2161 (1) ◽  
pp. 012064
Author(s):  
M Dhruv ◽  
R Sai Chandra Teja ◽  
R Sri Devi ◽  
S Nagesh Kumar
Keyword(s):  
Infectious Disease ◽  
Receptive Field ◽  
Ct Scan ◽  
Performance Metrics ◽  
Community Acquired Pneumonia ◽  
Field Structure ◽  
Mean Square ◽  
Severe Respiratory Failure ◽  
Kappa Score ◽  
Field Network

Abstract COVID-19 is an emerging infectious disease that has been rampant worldwide since its onset causing Lung irregularity and severe respiratory failure due to pneumonia. The Community-Acquired Pneumonia (CAP), Normal, and COVID-19 Computed Tomography (CT) scan images are classified using Involution Receptive Field Network from Large COVID-19 CT scan slice dataset. The proposed lightweight Involution Receptive Field Network (InRFNet) is spatial specific and channel-agnostic with Receptive Field structure to enhance the feature map extraction. The InRFNet model evaluation results show high training (99%) and validation (96%) accuracy. The performance metrics of the InRFNet model are Sensitivity (94.48%), Specificity (97.87%), Recall (96.34%), F1-score (96.33%), kappa score (94.10%), ROC-AUC (99.41%), mean square error (0.04), and the total number of parameters (33100).

COVID-19: how nuclear medicine can provide a differential diagnosis in very dubious case

Coronaviruses ◽  
2020 ◽  
Vol 01 ◽  
Author(s):  
Maria Silvia De Feo ◽  
Viviana Frantellizzi ◽  
Giuseppe De Vincentis
Keyword(s):  
Infectious Disease ◽  
Differential Diagnosis ◽  
Ct Scan ◽  
Imaging Technique ◽  
False Negative ◽  
Clinical Signs ◽  
Negative Results ◽  
Infectious Disease Department ◽  
False Negative Results ◽  
99Mtc Hmpao

Background: We present the case of a 55-year-old woman, admitted to the Infectious Disease Department of Policlinico Umberto I, Rome, in mid-March 2020, with suspicion of COVID-19 infection. Objective: The rRT-PCR was negative and the following CT scan, performed to exclude false-negative results and help diagnosis, was inconclusive. Methods: It was decided to submit the patient to 99mTc-HMPAO-labelled leukocyte scan. Results: This exam led to the diagnosis of infective endocarditis. Conclusion: In the present pandemic scenario, 99mTc-HMPAO-labelled leukocyte scan represents a reliable imaging technique for differential diagnosis with COVID-19 in patients with confusing clinical signs, possible false-negative rRT-PCR results and inconclusive CT scan.

DEVELOPMENT OF FEEDFORWARD RECEPTIVE FIELD STRUCTURE OF A SIMPLE CELL AND ITS CONTRIBUTION TO THE ORIENTATION SELECTIVITY: A MODELING STUDY

2005 ◽  
Vol 15 (01n02) ◽  
pp. 55-70 ◽  
Author(s):  
AKHIL R GARG ◽  
KLAUS OBERMAYER ◽  
BASABI BHAUMIK
Keyword(s):  
Receptive Field ◽  
Field Structure ◽  
Orientation Selectivity ◽  
Simple Cell ◽  
Homogeneous State ◽  
Synaptic Efficacy ◽  
Synaptic Interactions ◽  
Experimental Findings ◽  
Modeling Study

Recent experimental studies of hetero-synaptic interactions in various systems have shown the role of signaling in the plasticity, challenging the conventional understanding of Hebb's rule. It has also been found that activity plays a major role in plasticity, with neurotrophins acting as molecular signals translating activity into structural changes. Furthermore, role of synaptic efficacy in biasing the outcome of competition has also been revealed recently. Motivated by these experimental findings we present a model for the development of simple cell receptive field structure based on the competitive hetero-synaptic interactions for neurotrophins combined with cooperative hetero-synaptic interactions in the spatial domain. We find that with proper balance in competition and cooperation, the inputs from two populations (ON/OFF) of LGN cells segregate starting from the homogeneous state. We obtain segregated ON and OFF regions in simple cell receptive field. Our modeling study supports the experimental findings, suggesting the role of synaptic efficacy and the role of spatial signaling. We find that using this model we obtain simple cell RF, even for positively correlated activity of ON/OFF cells. We also compare different mechanism of finding the response of cortical cell and study their possible role in the sharpening of orientation selectivity. We find that degree of selectivity improvement in individual cells varies from case to case depending upon the structure of RF field and type of sharpening mechanism.

scholarly journals Parallel ON and OFF Cone Bipolar Inputs Establish Spatially Coextensive Receptive Field Structure of Blue-Yellow Ganglion Cells in Primate Retina

2009 ◽  
Vol 29 (26) ◽  
pp. 8372-8387 ◽  
Author(s):  
J. D. Crook ◽  
C. M. Davenport ◽  
B. B. Peterson ◽  
O. S. Packer ◽  
P. B. Detwiler ◽  
...  
Keyword(s):  
Receptive Field ◽  
Ganglion Cells ◽  
Field Structure

scholarly journals Attention operates uniformly throughout the classical receptive field and the surround

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Bram-Ernst Verhoef ◽  
John HR Maunsell
Keyword(s):  
Receptive Field ◽  
Cortical Neurons ◽  
Receptive Fields ◽  
Spatial Summation ◽  
Field Structure ◽  
Neuronal Responses ◽  
Large Variability ◽  
Area V4 ◽  
Classical Receptive Field ◽  
Spatially Varying

Shifting attention among visual stimuli at different locations modulates neuronal responses in heterogeneous ways, depending on where those stimuli lie within the receptive fields of neurons. Yet how attention interacts with the receptive-field structure of cortical neurons remains unclear. We measured neuronal responses in area V4 while monkeys shifted their attention among stimuli placed in different locations within and around neuronal receptive fields. We found that attention interacts uniformly with the spatially-varying excitation and suppression associated with the receptive field. This interaction explained the large variability in attention modulation across neurons, and a non-additive relationship among stimulus selectivity, stimulus-induced suppression and attention modulation that has not been previously described. A spatially-tuned normalization model precisely accounted for all observed attention modulations and for the spatial summation properties of neurons. These results provide a unified account of spatial summation and attention-related modulation across both the classical receptive field and the surround.

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