Proof of Concept for a Deep Learning Algorithm for Identification and Quantification of Key Microscopic Features in the Murine Model of DSS-Induced Colitis

2021 ◽  
pp. 019262332098780
Author(s):  
Agathe Bédard ◽  
Thomas Westerling-Bui ◽  
Aleksandra Zuraw
Keyword(s):  
Deep Learning ◽  
Acute Inflammation ◽  
Complex Disease ◽  
Learning Algorithm ◽  
Rapid Screening ◽  
Proof Of Concept ◽  
Entire Colon ◽  
Colon Tissue ◽  
Dss Colitis ◽  
Training Sets

Inflammatory bowel disease (IBD) is a complex disease which leads to life-threatening complications and decreased quality of life. The dextran sulfate sodium (DSS) colitis model in mice is known for rapid screening of candidate compounds. Efficacy assessment in this model relies partly on microscopic semiquantitative scoring, which is time-consuming and subjective. We hypothesized that deep learning artificial intelligence (AI) could be used to identify acute inflammation in H&E-stained sections in a consistent and quantitative manner. Training sets were established using ×20 whole slide images of the entire colon. Supervised training of a Convolutional Neural Network (CNN) was performed using a commercial AI platform to detect the entire colon tissue, the muscle and mucosa layers, and 2 categories within the mucosa (normal and acute inflammation E1). The training sets included slides of naive, vehicle-DSS and cyclosporine A-DSS mice. The trained CNN was able to segment, with a high level of concordance, the different tissue compartments in the 3 groups of mice. The segmented areas were used to determine the ratio of E1-affected mucosa to total mucosa. This proof-of-concept work shows promise to increase efficiency and decrease variability of microscopic scoring of DSS colitis when screening candidate compounds for IBD.

Deep learning algorithm for the classification of spirometries using flow-volume curves: proof of concept study

2021 ◽  
Author(s):  
Konstantinos Exarchos ◽  
Dimitrios Potonos ◽  
Agapi Aggelopoulou ◽  
Agni Sioutkou ◽  
Konstantinos Kostikas
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Flow Volume ◽  
Proof Of Concept ◽  
Concept Study ◽  
Deep Learning Algorithm

COVID-DETECT: A DEEP LEARNING APPROACH FOR CLASSIFICATION OF COVID-19 PNEUMONIA FROM LUNG SEGMENTED CHEST X-RAYS

2020 ◽  
pp. 2150010
Author(s):  
S. Rajkumar ◽  
P. V. Rajaraman ◽  
Haree Shankar Meganathan ◽  
V. Sapthagirivasan ◽  
K. Tejaswinee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Rapid Screening ◽  
X Rays ◽  
X Ray ◽  
Deep Learning Algorithm ◽  
X Ray Imaging ◽  
Before And After ◽  
Wide Availability ◽  
Novel Coronavirus

The novel coronavirus (COVID-19) was first reported in the Wuhan City of China in 2019 and became a pandemic. The outbreak has caused shocking effects to the people across the globe. It is important to screen a majority of the population in every country and for the respective governments to take appropriate action. There is a need for a rapid screening system to triage and recommend the patients for appropriate treatment. Chest X-ray imaging is one of the potential modalities, which has ample advantages such as wide availability even in the villages, portability, fast data sharing option from the point of capturing to the point of investigation, etc. The aim of the proposed work is to develop a deep learning algorithm for screening COVID-19 cases by leveraging the widely available X-ray imaging. We have built a deep learning Convolutional Neural Network model utilizing a combination of the public domain (open-source COVID-19) and private data (pneumonia and normal cases). The dataset was used before and after the segmentation of the lung region for training and testing. The outcome of the classification after lung segmentation resulted in significant superiority. The average accuracy achieved by the proposed system was 96%. The heat maps incorporated in the system were helpful for our radiologists to cross-verify whether the appropriate features are identified. This system (COVID-Detect) can be used in remote places in the countries affected by COVID-19 for mass screening of suspected cases and suggesting appropriate actions, such as recommending confirmatory tests.

Deep Learning Use for Differentiation of Low-grade vs High-Grade Glioma in Intraoperative Squash Smears

2021 ◽  
Author(s):  
Bhushan Diwakar Thombre ◽  
B N Nandeesh ◽  
Vikas Vazhayil ◽  
A R Prabhuraj
Keyword(s):  
Deep Learning ◽  
Diagnostic Tool ◽  
Learning Algorithm ◽  
High Grade Glioma ◽  
Rapid Screening ◽  
Training Dataset ◽  
Low Grade ◽  
High Grade ◽  
Current Standard ◽  
Feature Visualization

Abstract ObjectiveAutomated diagnosis using Artificial Intelligence (AI) techniques would be a useful addition to the intraoperative squash smear diagnosis. A robust diagnostic tool would enhance capabilities in centres where there is limited expertise for the diagnosis of intracranial lesions. The study aims to explore possibilities of deep learning technique-based models to classify squash smear images of glioma into high- and low-grade tumors.Methods500 Scanned images of squash smear were obtained intraoperatively and dataset was built. Image dataset was then pre-processed and fed into a CNN (Convolutional Neural Network) model for training and validation. The dataset consisted of 10,000 images of high (6000) and low (4000) grade gliomas, divided into three sets of training, validation and testing. ResultsCNN model based on deep learning algorithm was built and trained on training dataset to get accuracy of 96.2%. On a testing dataset which contains images previously unseen by trained model, it could achieve accuracies of 91% for diagnosing high grade glioma and 77% for low grade glioma. A positive predictive value of 86.6% and F1-score of 0.887 was achieved. Feature visualization technique was applied at the end to visualize regions of interest.ConclusionDeep Learning techniques can be applied as diagnostic tool if proper standardized images are obtained for reporting of squash smears of gliomas. The diagnostic accuracies of such tools can reach up to current standard diagnostic accuracies by conventional ways of reporting. Feature visualization techniques applied which can be used for rapid screening of slides or section of slide to assist in rapid diagnosis.

Classification and Recognition of Electronic Components Based on Improved Cooperative Semi-supervised Learning Algorithm

2020 ◽  
Vol 14 ◽  
Author(s):  
Dan Luo
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Supervised Learning ◽  
Image Recognition ◽  
Production Efficiency ◽  
Learning Algorithm ◽  
Electronic Components ◽  
Electron Device ◽  
Actual Recognition ◽  
The Subject

Background: As known that the semi-supervised algorithm is a classical algorithm in semi-supervised learning algorithm. Methods: In the paper, it proposed improved cooperative semi-supervised learning algorithm, and the algorithm process is presented in detailed, and it is adopted to predict unlabeled electronic components image. Results: In the experiments of classification and recognition of electronic components, it show that through the method the accuracy the proposed algorithm in electron device image recognition can be significantly improved, the improved algorithm can be used in the actual recognition process . Conclusion: With the continuous development of science and technology, machine vision and deep learning will play a more important role in people's life in the future. The subject research based on the identification of the number of components is bound to develop towards the direction of high precision and multi-dimension, which will greatly improve the production efficiency of electronic components industry.

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