Segmentation  of  Polyp Instruments  using  UNet  based deep  learning model

In this paper, we present a UNet architecture-based deep learning method that is used to segment polyp and instruments from the image data set provided in the MedAI Challenge2021. For the polyp segmentation task, we developed a UNet based algorithm for segmenting polyps in images taken from endoscopies. The main focus of this task is to achieve high segmentation metrics on the supplied test dataset. Similarly for the polyp segmentation task, in the instrument segmentation task, we have developed UNet based algorithms for segmenting instruments present in colonoscopy videos.

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Transfer learning with chest X-rays for ER patient classification

Scientific Reports ◽

10.1038/s41598-020-78060-4 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Jonathan Stubblefield ◽

Mitchell Hervert ◽

Jason L. Causey ◽

Jake A. Qualls ◽

Wei Dong ◽

...

Keyword(s):

Deep Learning ◽

Current Model ◽

Image Data ◽

Learning Model ◽

Image Features ◽

Clinical Feature ◽

Patient Classification ◽

X Rays ◽

Data Set ◽

Deep Learning Model

AbstractOne of the challenges with urgent evaluation of patients with acute respiratory distress syndrome (ARDS) in the emergency room (ER) is distinguishing between cardiac vs infectious etiologies for their pulmonary findings. We conducted a retrospective study with the collected data of 171 ER patients. ER patient classification for cardiac and infection causes was evaluated with clinical data and chest X-ray image data. We show that a deep-learning model trained with an external image data set can be used to extract image features and improve the classification accuracy of a data set that does not contain enough image data to train a deep-learning model. An analysis of clinical feature importance was performed to identify the most important clinical features for ER patient classification. The current model is publicly available with an interface at the web link: http://nbttranslationalresearch.org/.

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Cardiac or Infectious? Transfer Learning with Chest X-Rays for ER Patient Classification

10.1101/2020.04.11.20062091 ◽

2020 ◽

Cited By ~ 2

Author(s):

Jonathan Stubblefield ◽

Mitchell Hervert ◽

Jason Causey ◽

Jake Qualls ◽

Wei Dong ◽

...

Keyword(s):

Deep Learning ◽

Clinical Data ◽

Image Data ◽

Learning Model ◽

Patient Classification ◽

X Rays ◽

Data Set ◽

X Ray ◽

Chest X Ray ◽

Deep Learning Model

AbstractOne of the challenges with urgent evaluation of patients with acute respiratory distress syndrome (ARDS) in the emergency room (ER) is distinguishing between cardiac vs infectious etiologies for their pulmonary findings. We evaluated ER patient classification for cardiac and infection causes with clinical data and chest X-ray image data. We show that a deep-learning model trained with an external image data set can be used to extract image features and improve the classification accuracy of a data set that does not contain enough image data to train a deep-learning model. We also conducted clinical feature importance analysis and identified the most important clinical features for ER patient classification. This model can be upgraded to include a SARS-CoV-2 specific classification with COVID-19 patients data. The current model is publicly available with an interface at the web link: http://nbttranslationalresearch.org/.Data statementThe clinical data and chest x-ray image data for this study were collected and prepared by the residents and researchers of the Joint Translational Research Lab of Arkansas State University (A-State) and St. Bernards Medical Center (SBMC) Internal Medicine Residency Program. As data collection is on-going for the project stage-II of clinical testing, raw data is not currently available for data sharing to the public.EthicsThis study was approved by the St. Bernards Medical Center’s Institutional Review Board (IRB).

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NIMG-08. PREDICTION OF LOWER-GRADE GLIOMA MOLECULAR SUBTYPES USING DEEP LEARNING

Neuro-Oncology ◽

10.1093/neuonc/noaa215.621 ◽

2020 ◽

Vol 22 (Supplement_2) ◽

pp. ii148-ii148

Author(s):

Yoshihiro Muragaki ◽

Yutaka Matsui ◽

Takashi Maruyama ◽

Masayuki Nitta ◽

Taiichi Saito ◽

...

Keyword(s):

Deep Learning ◽

Cross Validation ◽

Molecular Subtype ◽

Learning Model ◽

Group Classification ◽

Training Dataset ◽

Lower Grade ◽

Test Dataset ◽

Ct Data ◽

Deep Learning Model

Abstract INTRODUCTION It is useful to know the molecular subtype of lower-grade gliomas (LGG) when deciding on a treatment strategy. This study aims to diagnose this preoperatively. METHODS A deep learning model was developed to predict the 3-group molecular subtype using multimodal data including magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT). The performance was evaluated using leave-one-out cross validation with a dataset containing information from 217 LGG patients. RESULTS The model performed best when the dataset contained MRI, PET, and CT data. The model could predict the molecular subtype with an accuracy of 96.6% for the training dataset and 68.7% for the test dataset. The model achieved test accuracies of 58.5%, 60.4%, and 59.4% when the dataset contained only MRI, MRI and PET, and MRI and CT data, respectively. The conventional method used to predict mutations in the isocitrate dehydrogenase (IDH) gene and the codeletion of chromosome arms 1p and 19q (1p/19q) sequentially had an overall accuracy of 65.9%. This is 2.8 percent point lower than the proposed method, which predicts the 3-group molecular subtype directly. CONCLUSIONS AND FUTURE PERSPECTIVE A deep learning model was developed to diagnose the molecular subtype preoperatively based on multi-modality data in order to predict the 3-group classification directly. Cross-validation showed that the proposed model had an overall accuracy of 68.7% for the test dataset. This is the first model to double the expected value for a 3-group classification problem, when predicting the LGG molecular subtype. We plan to apply the techniques of heat map and/or segmentation for an increase in prediction accuracy.

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Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics

Cancers ◽

10.3390/cancers14010012 ◽

2021 ◽

Vol 14 (1) ◽

pp. 12

Author(s):

Jose M. Castillo T. ◽

Muhammad Arif ◽

Martijn P. A. Starmans ◽

Wiro J. Niessen ◽

Chris H. Bangma ◽

...

Keyword(s):

Prostate Cancer ◽

Deep Learning ◽

Characteristic Curve ◽

Model Development ◽

Learning Model ◽

Multiparametric Mri ◽

Data Sets ◽

Data Set ◽

Test Sets ◽

Deep Learning Model

The computer-aided analysis of prostate multiparametric MRI (mpMRI) could improve significant-prostate-cancer (PCa) detection. Various deep-learning- and radiomics-based methods for significant-PCa segmentation or classification have been reported in the literature. To be able to assess the generalizability of the performance of these methods, using various external data sets is crucial. While both deep-learning and radiomics approaches have been compared based on the same data set of one center, the comparison of the performances of both approaches on various data sets from different centers and different scanners is lacking. The goal of this study was to compare the performance of a deep-learning model with the performance of a radiomics model for the significant-PCa diagnosis of the cohorts of various patients. We included the data from two consecutive patient cohorts from our own center (n = 371 patients), and two external sets of which one was a publicly available patient cohort (n = 195 patients) and the other contained data from patients from two hospitals (n = 79 patients). Using multiparametric MRI (mpMRI), the radiologist tumor delineations and pathology reports were collected for all patients. During training, one of our patient cohorts (n = 271 patients) was used for both the deep-learning- and radiomics-model development, and the three remaining cohorts (n = 374 patients) were kept as unseen test sets. The performances of the models were assessed in terms of their area under the receiver-operating-characteristic curve (AUC). Whereas the internal cross-validation showed a higher AUC for the deep-learning approach, the radiomics model obtained AUCs of 0.88, 0.91 and 0.65 on the independent test sets compared to AUCs of 0.70, 0.73 and 0.44 for the deep-learning model. Our radiomics model that was based on delineated regions resulted in a more accurate tool for significant-PCa classification in the three unseen test sets when compared to a fully automated deep-learning model.

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Simulation and Recognition of Concrete Lining Infiltration Degree via an Indoor Experiment

Geofluids ◽

10.1155/2020/8873315 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Dongsheng Wang ◽

Jun Feng ◽

Xinpeng Zhao ◽

Yeping Bai ◽

Yujie Wang ◽

...

Keyword(s):

Deep Learning ◽

Cement Mortar ◽

Recognition Accuracy ◽

Image Data ◽

Concrete Lining ◽

Learning Method ◽

Recognition Method ◽

Data Set ◽

Recognition Model ◽

High Recognition Accuracy

It is difficult to form a method for recognizing the degree of infiltration of a tunnel lining. To solve this problem, we propose a recognition method by using a deep convolutional neural network. We carry out laboratory tests, prepare cement mortar specimens with different saturation levels, simulate different degrees of infiltration of tunnel concrete linings, and establish an infrared thermal image data set with different degrees of infiltration. Then, based on a deep learning method, the data set is trained using the Faster R-CNN+ResNet101 network, and a recognition model is established. The experiments show that the recognition model established by the deep learning method can be used to select cement mortar specimens with different degrees of infiltration by using an accurately minimized rectangular outer frame. This model shows that the classification recognition model for tunnel concrete lining infiltration established by the indoor experimental method has high recognition accuracy.

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Ground-truth uncertainty-aware metrics for machine learning applications on seismic image interpretation: Application to faults and horizon extraction

The Leading Edge ◽

10.1190/tle39100734.1 ◽

2020 ◽

Vol 39 (10) ◽

pp. 734-741

Author(s):

Sébastien Guillon ◽

Frédéric Joncour ◽

Pierre-Emmanuel Barrallon ◽

Laurent Castanié

Keyword(s):

Deep Learning ◽

Image Interpretation ◽

Ground Truth ◽

Learning Model ◽

Seismic Interpretation ◽

Data Set ◽

Seismic Image ◽

Machine Learning Applications ◽

Deep Learning Model

We propose new metrics to measure the performance of a deep learning model applied to seismic interpretation tasks such as fault and horizon extraction. Faults and horizons are thin geologic boundaries (1 pixel thick on the image) for which a small prediction error could lead to inappropriately large variations in common metrics (precision, recall, and intersection over union). Through two examples, we show how classical metrics could fail to indicate the true quality of fault or horizon extraction. Measuring the accuracy of reconstruction of thin objects or boundaries requires introducing a tolerance distance between ground truth and prediction images to manage the uncertainties inherent in their delineation. We therefore adapt our metrics by introducing a tolerance function and illustrate their ability to manage uncertainties in seismic interpretation. We compare classical and new metrics through different examples and demonstrate the robustness of our metrics. Finally, we show on a 3D West African data set how our metrics are used to tune an optimal deep learning model.

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Supporting risk management in the Caribbean by application of Deep Learning for object classification of aerial imagery with MATLAB

10.5194/egusphere-egu2020-9232 ◽

2020 ◽

Author(s):

Sebastian Bomberg ◽

Neha Goel

Keyword(s):

Risk Management ◽

Deep Learning ◽

Gpu Computing ◽

Learning Model ◽

Aerial Imagery ◽

Validation Data ◽

Data Set ◽

Roof Material ◽

Caribbean Islands ◽

Deep Learning Model

<p>The presented work focuses on disaster risk management of cities which are prone to natural hazards. Based on aerial imagery captured by drones of regions in Caribbean islands, we show how to process and automatically identify roof material of individual structures using a deep learning model. Deep learning refers to a machine learning technique using deep artificial neural networks. Unlike other techniques, deep learning does not necessarily require feature engineering but may process raw data directly. The outcome of this assessment can be used for steering risk mitigations measures, creating risk hazard maps or advising municipal bodies or help organizations on investing their resources in rebuilding reinforcements. Data at hand consists of images in BigTIFF format and GeoJSON files including the building footprint, unique building ID and roof material labels. We demonstrate how to use MATLAB and its toolboxes for processing large image files that do not fit in computer memory. Based on this, we perform the training of a deep learning model to classify roof material present in the images. We achieve this by subjecting a pretrained ResNet-18 neural network to transfer learning. Training is further accelerated by means of GPU computing. The accuracy computed from a validation data set achieved by this baseline model is 74%. Further tuning of hyperparameters is expected to improve accuracy significantly.</p>

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Predicting the Mortality and Readmission of In-Hospital Cardiac Arrest Patients With Electronic Health Records: A Machine Learning Approach (Preprint)

10.2196/preprints.27798 ◽

2021 ◽

Author(s):

Chien-Yu Chi ◽

Shuang Ao ◽

Adrian Winkler ◽

Kuan-Chun Fu ◽

Jie Xu ◽

...

Keyword(s):

Cardiac Arrest ◽

Deep Learning ◽

Operating Characteristic ◽

Outcome Prediction ◽

Learning Model ◽

Data Set ◽

Clinical Records ◽

Care Costs ◽

Deep Learning Model ◽

Hospital Cardiac Arrest

BACKGROUND In-hospital cardiac arrest (IHCA) is associated with high mortality and health care costs in the recovery phase. Predicting adverse outcome events, including readmission, improves the chance for appropriate interventions and reduces health care costs. However, studies related to the early prediction of adverse events of IHCA survivors are rare. Therefore, we used a deep learning model for prediction in this study. OBJECTIVE This study aimed to demonstrate that with the proper data set and learning strategies, we can predict the 30-day mortality and readmission of IHCA survivors based on their historical claims. METHODS National Health Insurance Research Database claims data, including 168,693 patients who had experienced IHCA at least once and 1,569,478 clinical records, were obtained to generate a data set for outcome prediction. We predicted the 30-day mortality/readmission after each current record (ALL-mortality/ALL-readmission) and 30-day mortality/readmission after IHCA (cardiac arrest [CA]-mortality/CA-readmission). We developed a hierarchical vectorizer (HVec) deep learning model to extract patients’ information and predict mortality and readmission. To embed the textual medical concepts of the clinical records into our deep learning model, we used Text2Node to compute the distributed representations of all medical concept codes as a 128-dimensional vector. Along with the patient’s demographic information, our novel HVec model generated embedding vectors to hierarchically describe the health status at the record-level and patient-level. Multitask learning involving two main tasks and auxiliary tasks was proposed. As CA-mortality and CA-readmission were rare, person upsampling of patients with CA and weighting of CA records were used to improve prediction performance. RESULTS With the multitask learning setting in the model learning process, we achieved an area under the receiver operating characteristic of 0.752 for CA-mortality, 0.711 for ALL-mortality, 0.852 for CA-readmission, and 0.889 for ALL-readmission. The area under the receiver operating characteristic was improved to 0.808 for CA-mortality and 0.862 for CA-readmission after solving the extremely imbalanced issue for CA-mortality/CA-readmission by upsampling and weighting. CONCLUSIONS This study demonstrated the potential of predicting future outcomes for IHCA survivors by machine learning. The results showed that our proposed approach could effectively alleviate data imbalance problems and train a better model for outcome prediction.

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Multi-Task Model for Esophageal Lesion Analysis Using Endoscopic Images: Classification with Image Retrieval and Segmentation with Attention

Sensors ◽

10.3390/s22010283 ◽

2021 ◽

Vol 22 (1) ◽

pp. 283

Author(s):

Xiaoyuan Yu ◽

Suigu Tang ◽

Chak Fong Cheang ◽

Hon Ho Yu ◽

I Cheong Choi

Keyword(s):

Deep Learning ◽

Image Retrieval ◽

Learning Model ◽

Endoscopic Images ◽

Proposed Model ◽

Esophageal Lesion ◽

Lesion Analysis ◽

Diagnosis Accuracy ◽

Segmentation Task ◽

Deep Learning Model

The automatic analysis of endoscopic images to assist endoscopists in accurately identifying the types and locations of esophageal lesions remains a challenge. In this paper, we propose a novel multi-task deep learning model for automatic diagnosis, which does not simply replace the role of endoscopists in decision making, because endoscopists are expected to correct the false results predicted by the diagnosis system if more supporting information is provided. In order to help endoscopists improve the diagnosis accuracy in identifying the types of lesions, an image retrieval module is added in the classification task to provide an additional confidence level of the predicted types of esophageal lesions. In addition, a mutual attention module is added in the segmentation task to improve its performance in determining the locations of esophageal lesions. The proposed model is evaluated and compared with other deep learning models using a dataset of 1003 endoscopic images, including 290 esophageal cancer, 473 esophagitis, and 240 normal. The experimental results show the promising performance of our model with a high accuracy of 96.76% for the classification and a Dice coefficient of 82.47% for the segmentation. Consequently, the proposed multi-task deep learning model can be an effective tool to help endoscopists in judging esophageal lesions.

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Mobile Deep Learning System That Calculates UVI Using Illuminance Value of User’s Location

Sensors ◽

10.3390/s21041227 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1227

Author(s):

Seung-Taek Oh ◽

Deog-Hyeon Ga ◽

Jae-Hyun Lim

Keyword(s):

Deep Learning ◽

Mobile Devices ◽

Mobile Device ◽

Local Level ◽

Learning Model ◽

Learning System ◽

Ultraviolet Rays ◽

Optimum Number ◽

Data Set ◽

Deep Learning Model

Ultraviolet rays are closely related with human health and, recently, optimum exposure to the UV rays has been recommended, with growing importance being placed on correct UV information. However, many countries provide UV information services at a local level, which makes it impossible for individuals to acquire user-based, accurate UV information unless individuals operate UV measurement devices with expertise on the relevant field for interpretation of the measurement results. There is a limit in measuring ultraviolet rays’ information by the users at their respective locations. Research about how to utilize mobile devices such as smartphones to overcome such limitation is also lacking. This paper proposes a mobile deep learning system that calculates UVI based on the illuminance values at the user’s location obtained with mobile devices’ help. The proposed method analyzed the correlation between illuminance and UVI based on the natural light DB collected through the actual measurements, and the deep learning model’s data set was extracted. After the selection of the input variables to calculate the correct UVI, the deep learning model based on the TensorFlow set with the optimum number of layers and number of nodes was designed and implemented, and learning was executed via the data set. After the data set was converted to the mobile deep learning model to operate under the mobile environment, the converted data were loaded on the mobile device. The proposed method enabled providing UV information at the user’s location through a mobile device on which the illuminance sensors were loaded even in the environment without UVI measuring equipment. The comparison of the experiment results with the reference device (spectrometer) proved that the proposed method could provide UV information with an accuracy of 90–95% in the summers, as well as in winters.

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