scholarly journals Automatic Detection of Airway Invasion from Videofluoroscopy via Deep Learning Technology

2020 ◽  
Vol 10 (18) ◽  
pp. 6179
Author(s):  
Seong Jae Lee ◽  
Joo Young Ko ◽  
Hyun Il Kim ◽  
Sang-Il Choi
Keyword(s):  
Deep Learning ◽  
Present Model ◽  
Spinal Column ◽  
Region Of Interest ◽  
Learning Technology ◽  
Accurate Analysis ◽  
Human Errors ◽  
Airway Invasion ◽  
Human Effort ◽  
Deep Learning Model

In dysphagia, food materials frequently invade the laryngeal airway, potentially resulting in serious consequences, such as asphyxia or pneumonia. The VFSS (videofluoroscopic swallowing study) procedure can be used to visualize the occurrence of airway invasion, but its reliability is limited by human errors and fatigue. Deep learning technology may improve the efficiency and reliability of VFSS analysis by reducing the human effort required. A deep learning model has been developed that can detect airway invasion from VFSS images in a fully automated manner. The model consists of three phases: (1) image normalization, (2) dynamic ROI (region of interest) determination, and (3) airway invasion detection. Noise induced by movement and learning from unintended areas is minimized by defining a “dynamic” ROI with respect to the center of the cervical spinal column as segmented using U-Net. An Xception module, trained on a dataset consisting of 267,748 image frames obtained from 319 VFSS video files, is used for the detection of airway invasion. The present model shows an overall accuracy of 97.2% in classifying image frames and 93.2% in classifying video files. It is anticipated that the present model will enable more accurate analysis of VFSS data.

scholarly journals A Framework for Automatic Burn Image Segmentation and Burn Depth Diagnosis Using Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hao Liu ◽  
Keqiang Yue ◽  
Siyi Cheng ◽  
Wenjun Li ◽  
Zhihui Fu
Keyword(s):  
Deep Learning ◽  
Total Body Surface Area ◽  
High Morbidity ◽  
Learning Technology ◽  
Human Errors ◽  
Burn Wounds ◽  
Output Structure ◽  
Network Output ◽  
Total Body ◽  
Burn Depth

Burn is a common traumatic disease with high morbidity and mortality. The treatment of burns requires accurate and reliable diagnosis of burn wounds and burn depth, which can save lives in some cases. However, due to the complexity of burn wounds, the early diagnosis of burns lacks accuracy and difference. Therefore, we use deep learning technology to automate and standardize burn diagnosis to reduce human errors and improve burn diagnosis. First, the burn dataset with detailed burn area segmentation and burn depth labelling is created. Then, an end-to-end framework based on deep learning method for advanced burn area segmentation and burn depth diagnosis is proposed. The framework is firstly used to segment the burn area in the burn images. On this basis, the calculation of the percentage of the burn area in the total body surface area (TBSA) can be realized by extending the network output structure and the labels of the burn dataset. Then, the framework is used to segment multiple burn depth areas. Finally, the network achieves the best result with IOU of 0.8467 for the segmentation of burn and no burn area. And for multiple burn depth areas segmentation, the best average IOU is 0.5144.

scholarly journals In Machines We Trust: Are Robo-Advisers More Trustworthy Than Human Financial Advisers?

2019 ◽  
pp. 129-141 ◽  
Author(s):  
Hui Xian Chia
Keyword(s):  
Deep Learning ◽  
Black Box ◽  
Best Interests ◽  
Decision Making Process ◽  
Learning Technology ◽  
Technical Expertise ◽  
Learning Agents ◽  
The People ◽  
Self Interest ◽  
Deep Learning Model

This article examines the use of artificial intelligence (AI) and deep learning, specifically, to create financial robo-advisers. These machines have the potential to be perfectly honest fiduciaries, acting in their client’s best interests without conflicting self-interest or greed, unlike their human counterparts. However, the application of AI technology to create financial robo-advisers is not without risk. This article will focus on the unique risks posed by deep learning technology. One of the main fears regarding deep learning is that it is a “black box”, its decision-making process is opaque and not open to scrutiny even by the people who developed it. This poses a significant challenge to financial regulators, whom would not be able to examine the underlying rationale and rules of the robo-adviser to determine its safety for public use. The rise of deep learning has been met with calls for ‘explainability’ of how deep learning agents make their decisions. This paper argues that greater explainability can be achieved by describing the ‘personality’ of deep learning robo-advisers, and further proposes a framework for describing the parameters of the deep learning model using concepts that can be readily understood by people without technical expertise. This regards whether the robo-adviser is ‘greedy’, ‘selfish’ or ‘prudent’. Greater understanding will enable regulators and consumers to better judge the safety and suitability of deep learning financial robo-advisers.

Research on Methodology of Correlation Analysis of Sci-Tech Literature Based on Deep Learning Technology in the Big Data

2018 ◽  
Vol 29 (3) ◽  
pp. 67-88 ◽  
Author(s):  
Wen Zeng ◽  
Hongjiao Xu ◽  
Hui Li ◽  
Xiang Li
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Correlation Analysis ◽  
Vector Space ◽  
Input Word ◽  
Learning Technology ◽  
Depth Analysis ◽  
Different Types ◽  
High Level ◽  
Deep Learning Model

In the big data era, it is a great challenge to identify high-level abstract features out of a flood of sci-tech literature to achieve in-depth analysis of data. The deep learning technology has developed rapidly and achieved applications in many fields, but has rarely been utilized in the research of sci-tech literature data. This article introduced the presentation method of vector space of terminologies in sci-tech literature based on the deep learning model. It explored and adopted a deep AE model to reduce the dimensionality of input word vector feature. Also put forward is the methodology of correlation analysis of sci-tech literature based on deep learning technology. The experimental results showed that the processing of sci-tech literature data could be simplified into the computation of vectors in the multi-dimensional vector space, and the similarity in vector space could be used to represent similarity in text semantics. The correlation analysis of subject contents between sci-tech literatures of the same or different types can be made using this method.

scholarly journals Integrated Relaxation Pressure Classification and Probe Positioning Failure Detection in High-Resolution Esophageal Manometry Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 253
Author(s):  
Zoltan Czako ◽  
Teodora Surdea-Blaga ◽  
Gheorghe Sebestyen ◽  
Anca Hangan ◽  
Dan Lucian Dumitrascu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Resolution ◽  
Esophageal Manometry ◽  
Region Of Interest ◽  
Failure Detection ◽  
Final Diagnosis ◽  
Exact Moment ◽  
Integrated Relaxation Pressure ◽  
Deep Learning Model

High-resolution esophageal manometry is used for the study of esophageal motility disorders, with the help of catheters with up to 36 sensors. Color pressure topography plots are generated and analyzed and using the Chicago algorithm a final diagnosis is established. One of the main parameters in this algorithm is integrated relaxation pressure (IRP). The procedure is time consuming. Our aim was to firstly develop a machine learning based solution to detect probe positioning failure and to create a classifier to automatically determine whether the IRP is in the normal range or higher than the cut-off, based solely on the raw images. The first step was the preprocessing of the images, by finding the region of interest—the exact moment of swallowing. Afterwards, the images were resized and rescaled, so they could be used as input for deep learning models. We used the InceptionV3 deep learning model to classify the images as correct or failure in catheter positioning and to determine the exact class of the IRP. The accuracy of the trained convolutional neural networks was above 90% for both problems. This work is just the first step in fully automating the Chicago Classification, reducing human intervention.

scholarly journals Detecting orientation of Brain MR scans using deep learning

2021 ◽  
Author(s):  
Chinmay Singhal ◽  
Nihit Gupta ◽  
Anouk Stein ◽  
Quan Zhou ◽  
Leon Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Free Text ◽  
Learning Models ◽  
Human Errors ◽  
Linguistic Barriers ◽  
Model Training ◽  
The Impact ◽  
Deep Learning Model ◽  
Made In

AbstractThere has been a steady escalation in the impact of Artificial Intelligence (AI) on Healthcare along with an increasing amount of progress being made in this field. While many entities are working on the development of significant deep learning models for the diagnosis of brain-related diseases, identifying precise images needed for model training and inference tasks is limited due to variation in DICOM fields which use free text to define things like series description, sequence and orientation [1]. Detecting the orientation of brain MR scans (Axial/Sagittal/Coronal) remains a challenge due to these variations caused by linguistic barriers, human errors and de-identification - essentially rendering the tags unreliable [2, 3, 4]. In this work, we propose a deep learning model that identifies the orientation of brain MR scans with near perfect accuracy.

Research on Methodology of Correlation Analysis of Sci-Tech Literature Based on Deep Learning Technology in the Big Data

2020 ◽  
pp. 1524-1546
Author(s):  
Wen Zeng ◽  
Hongjiao Xu ◽  
Hui Li ◽  
Xiang Li
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Correlation Analysis ◽  
Vector Space ◽  
Input Word ◽  
Learning Technology ◽  
Depth Analysis ◽  
Presentation Method ◽  
High Level ◽  
Deep Learning Model

In the big data era, it is a great challenge to identify high-level abstract features out of a flood of sci-tech literature to achieve in-depth analysis of data. The deep learning technology has developed rapidly and achieved applications in many fields, but has rarely been utilized in the research of sci-tech literature data. This article introduced the presentation method of vector space of terminologies in sci-tech literature based on the deep learning model. It explored and adopted a deep AE model to reduce the dimensionality of input word vector feature. Also put forward is the methodology of correlation analysis of sci-tech literature based on deep learning technology. The experimental results showed that the processing of sci-tech literature data could be simplified into the computation of vectors in the multi-dimensional vector space, and the similarity in vector space could be used to represent similarity in text semantics. The correlation analysis of subject contents between sci-tech literatures of the same or different types can be made using this method.

Implementation and Practice of Deep Learning-Based Instance Segmentation Algorithm for Quantification of Hepatic Fibrosis at Whole Slide Level in Sprague-Dawley Rats

2021 ◽  
pp. 019262332110571
Author(s):  
Ji-Hee Hwang ◽  
Hyun-Ji Kim ◽  
Heejin Park ◽  
Byoung-Seok Lee ◽  
Hwa-Young Son ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Hepatic Fibrosis ◽  
Ground Truth ◽  
Learning Technology ◽  
Sprague Dawley ◽  
Sprague Dawley Rats ◽  
Deep Learning Model ◽  
Instance Segmentation ◽  
Whole Slide Images

Exponential development in artificial intelligence or deep learning technology has resulted in more trials to systematically determine the pathological diagnoses using whole slide images (WSIs) in clinical and nonclinical studies. In this study, we applied Mask Regions with Convolution Neural Network (Mask R-CNN), a deep learning model that uses instance segmentation, to detect hepatic fibrosis induced by N-nitrosodimethylamine (NDMA) in Sprague-Dawley rats. From 51 WSIs, we collected 2011 cropped images with hepatic fibrosis annotations. Training and detection of hepatic fibrosis via artificial intelligence methods was performed using Tensorflow 2.1.0, powered by an NVIDIA 2080 Ti GPU. From the test process using tile images, 95% of model accuracy was verified. In addition, we validated the model to determine whether the predictions by the trained model can reflect the scoring system by the pathologists at the WSI level. The validation was conducted by comparing the model predictions in 18 WSIs at 20× and 10× magnifications with ground truth annotations and board-certified pathologists. Predictions at 20× showed a high correlation with ground truth ( R 2 = 0.9660) and a good correlation with the average fibrosis rank by pathologists ( R 2 = 0.8887). Therefore, the Mask R-CNN algorithm is a useful tool for detecting and quantifying pathological findings in nonclinical studies.

scholarly journals Improving the efficiency of using deep learning model to determine shoreline position in high-resolution satellite imagery

2021 ◽  
Vol 310 ◽  
pp. 04002
Author(s):  
Nguyen Thanh Doan
Keyword(s):  
Deep Learning ◽  
Satellite Image ◽  
Mekong Delta ◽  
Learning Model ◽  
Training Dataset ◽  
Learning Technology ◽  
Model Training ◽  
High Resolution Satellite Imagery ◽  
The Impact ◽  
Deep Learning Model

Nowaday, expanding the application of deep learning technology is attracting attention of many researchers in the field of remote sensing. This paper presents methodology of using deep convolutional neural network model to determine the position of shoreline on Sentinel 2 satellite image. The methodology also provides techniques to reduce model retraining while ensuring the accuracy of the results. Methodological evaluation and analysis were conducted in the Mekong Delta region. The results from the study showed that interpolating the input images and calibrating the result thresholds improve accuracy and allow the trained deep learning model to externally test different images. The paper also evaluates the impact of the training dataset on the quality of the results obtained. Suggestions are also given for the number of files in the training dataset, as well as the information used for model training to solve the shoreline detection problem.

scholarly journals MedFuseNet: An attention-based multimodal deep learning model for visual question answering in the medical domain

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dhruv Sharma ◽  
Sanjay Purushotham ◽  
Chandan K. Reddy
Keyword(s):  
Deep Learning ◽  
Question Answering ◽  
Medical Images ◽  
Learning Model ◽  
Training Data ◽  
Human Errors ◽  
Medical Practitioners ◽  
Visual Question Answering ◽  
Deep Learning Model ◽  
Real World Problems

AbstractMedical images are difficult to comprehend for a person without expertise. The scarcity of medical practitioners across the globe often face the issue of physical and mental fatigue due to the high number of cases, inducing human errors during the diagnosis. In such scenarios, having an additional opinion can be helpful in boosting the confidence of the decision maker. Thus, it becomes crucial to have a reliable visual question answering (VQA) system to provide a ‘second opinion’ on medical cases. However, most of the VQA systems that work today cater to real-world problems and are not specifically tailored for handling medical images. Moreover, the VQA system for medical images needs to consider a limited amount of training data available in this domain. In this paper, we develop MedFuseNet, an attention-based multimodal deep learning model, for VQA on medical images taking the associated challenges into account. Our MedFuseNet aims at maximizing the learning with minimal complexity by breaking the problem statement into simpler tasks and predicting the answer. We tackle two types of answer prediction—categorization and generation. We conducted an extensive set of quantitative and qualitative analyses to evaluate the performance of MedFuseNet. Our experiments demonstrate that MedFuseNet outperforms the state-of-the-art VQA methods, and that visualization of the captured attentions showcases the intepretability of our model’s predicted results.

scholarly journals A DEEP LEARNING STUDY OF EXTRACTING NAVIGATION AREA FROM CAD BLUEPRINTS

2018 ◽  
Vol IV-4 ◽  
pp. 155-162
Author(s):  
L. Niu ◽  
Y. Q. Song ◽  
J. Su ◽  
H. M. Zhang
Keyword(s):  
Deep Learning ◽  
Model Performance ◽  
Geometric Approach ◽  
Learning Approach ◽  
Learning Technology ◽  
Result Section ◽  
Detection Model ◽  
Regular Shape ◽  
Deep Learning Model ◽  
Test Result

<p><strong>Abstract.</strong> Deep learning technology is a cutting edge topic of AI region, and draws more attention from photogrammetry and remote sensing society. In this study, we strive to combine deep learning with CAD designs to extract navigation area (room). To this, we mark more than 200 2D building blueprint in CAD forms to construct the learning set to train object detection model based on TensorFlow. This model is the faster R-CNN inception v2 model from COCO dataset. The test and result section is composed of three parts: First part demonstrates the model performance on learning dataset; second part applies the generated model to extract rooms from untrained raw CAD blueprints; Third part covers the comparison between deep learning extracted result and geometric based algorithm extracted result. Test result shows that the deep learning approach could achieve higher accuracy than geometric approach under regular shape situations. In conclusion, we have proposed a well-trained deep learning model that could be utilized to construct a schema of the navigation area for 2D CAD blueprints.</p>

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