Foot Parameters Extraction using Deep Learning based Regression Model

2021 ◽  
Author(s):  
Jeongrok Yun ◽  
Sungkuk Chun ◽  
Hoemin Kim ◽  
Un Yong Kim
Keyword(s):  
Deep Learning ◽  
Regression Model ◽  
Parameters Extraction

A hybrid modelling method for time series forecasting based on a linear regression model and deep learning

2019 ◽  
Vol 49 (8) ◽  
pp. 3002-3015 ◽  
Author(s):  
Wenquan Xu ◽  
Hui Peng ◽  
Xiaoyong Zeng ◽  
Feng Zhou ◽  
Xiaoying Tian ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Time Series Forecasting ◽  
Hybrid Modelling ◽  
Modelling Method

Determination of biologic and prognostic feature scores from whole slide histology images using deep learning.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e17527-e17527 ◽  
Author(s):  
Okyaz Eminaga ◽  
Mahmoud Abbas ◽  
Axel Semjonow ◽  
James D Brooks ◽  
Daniel Rubin
Keyword(s):  
Deep Learning ◽  
Androgen Receptor ◽  
Regression Model ◽  
Cox Regression ◽  
Risk Group ◽  
Receptor Protein ◽  
Gleason Grade ◽  
Cox Regression Model ◽  
Survival Analyses ◽  
Histopathological Feature

e17527 Background: In cancer, histopathology is a reflection of the underlying molecular changes in the cancer cells and provides prognostic information on the risk of disease progression. Therefore, whole slide images may harbor histopathological features that have a biological association and are prognostic. Methods: This study has extracted histopathological feature scores generated from hematoxylin and eosin (HE) histology images based on deep learning models developed for the detection of pathological findings related to prostate cancer (PCa). Correlation analyses between the histopathological feature scores and the most relevant genomic alterations related to PCa were performed based on the original results and diagnostic histology images from TCGA PRAD study (n = 251). We extracted feature scores from tumor lesions after applying tumor segmentation and several data transformation using five models developed for detection of cribriform or ductal morphologies, Gleason patterns 3 and 4, and the presumed tumor precursor. For prognostic evaluation, we performed survival analyses of 371 patients from the TCGA PRAD dataset with biochemical recurrence (BCR) using a Cox regression model, Kaplan Meier (KM) curves. We applied the bootstrapping resampling for the uncertainty evaluation and C-statistics for the randomness measurement. Results: The feature scores were significantly correlated with the androgen receptor protein expression, an androgen-signaling score, mRNA expression, and androgen receptor splice variant 7. In addition, feature scores were associated with SPINK1 overexpression, the heterozygous loss of TP53, and SPOP mutations. Additionally, the mRNA and miRNA clusters identified by the TCGA research team for PCa. These features were independent of Gleason grade and were non-random. The survival analyses revealed that a model, including three of five feature scores, achieved a c-index of 0.706 (95% CI: 0.606-0.779). The KM curve showed that these risk groups based on the Cox regression model are significantly discriminative (Log-rank P-value < 0.0001). The low-risk group (n = 177) achieved a 2-year BCR-free survival rate (BFS) of 97.4% (95% CI: 94.9 - 100.0%) and a 5-year PFS of 88.3% (95% CI: 80.6 - 96.7%). In contrast, the high-risk group (n = 194) showed a 2-year PFS of 86.3% (95% CI: 81.1 - 91.8%) and a 5-year BFS of 66.9% (95% CI: 54.6 - 0.82.1%). Conclusions: Our findings uncover the potential of feature scores from histology images as digital biomarkers in precision medicine and as an expanding utility for digital pathology.

An Efficient Deep Learning-Based Workflow Incorporating a Reduced Physics Model for Subsurface Imaging in Unconventional Reservoirs

2021 ◽  
Author(s):  
Tsubasa Onishi ◽  
Hongquan Chen ◽  
Akhil Datta-Gupta ◽  
Srikanta Mishra
Keyword(s):  
Deep Learning ◽  
Regression Model ◽  
Latent Variables ◽  
Measured Data ◽  
Unconventional Reservoirs ◽  
Field Scale ◽  
Data Types ◽  
Fast Marching ◽  
Drainage Volume ◽  
Physics Model

Abstract We present a novel deep learning-based workflow incorporating a reduced physics model that can efficiently visualize well drainage volume and pressure front propagation in unconventional reservoirs in near real-time. The visualizations can be readily used for qualitative and quantitative characterization and forecasting of unconventional reservoirs. Our aim is to develop an efficient workflow that allows us to ‘see’ within the subsurface given measured data, such as production data. The most simplistic way to achieve the goal will be to merely train a deep learning-based regression model where the input consists of some measured data, and the output is a subsurface image, such as pressure field. However, the high output dimension that corresponds to spatio-temporal steps makes the training inefficient. To address this challenge, an autoencoder network is applied to discover lower dimensional latent variables that represent high dimensional output images. In our approach, the regression model is trained to predict latent variables, instead of directly constructing an image. In the prediction step, the trained regression model first predicts latent variables given measured data, then the latent variables will be used as inputs of the trained decoder to generate a subsurface image. In addition, fast marching-method (FMM)-based rapid simulation workflow which transforms original 2D or 3D problems into 1D problems is used in place of full-physics simulation to efficiently generate datasets for training. The capability of the FMM-based rapid simulation allows us to generate sufficient datasets within realistic simulation times, even for field scale applications. We first demonstrate the proposed approach using a simple illustrative example. Next, the approach is applied to a field scale reservoir model built after the publicly available data on the Hydraulic Fracturing Test Site-I (HFTS-I), which is sufficiently complex to demonstrate the power and efficacy of the approach. We will further demonstrate the utility of the approach to account for subsurface uncertainty. Our approach, for the first time, allows data-driven visualization of unconventional well drainage volume in 3D. The novelty of our approach is the framework which combines the strengths of deep learning-based models and the FMM-based rapid simulation. The workflow has flexibility to incorporate various spatial and temporal data types.

scholarly journals Deep learning classification and regression models for temperature values on a simulated fibre specklegram sensor

2021 ◽  
Vol 2139 (1) ◽  
pp. 012001
Author(s):  
J D Arango ◽  
V H Aristizabal ◽  
J F Carrasquilla ◽  
J A Gomez ◽  
J C Quijano ◽  
...  
Keyword(s):  
Deep Learning ◽  
Regression Model ◽  
Regression Models ◽  
Absolute Error ◽  
Measurement Range ◽  
Classification Model ◽  
Alternative Methods ◽  
The Finite Element Method ◽  
Temperature Range ◽  
Modal Interference

Abstract Fiber optic specklegram sensors use the modal interference pattern (or specklegram) to determine the magnitude of a disturbance. The most used interrogation methods for these sensors have focused on point measurements of intensity or correlations between specklegrams, with limitations in sensitivity and useful measurement range. To investigate alternative methods of specklegram interrogation that improve the performance of the fiber specklegram sensors, we implemented and compared two deep learning models: a classification model and a regression model. To test and train the models, we use physical-optical models and simulations by the finite element method to create a database of specklegram images, covering the temperature range between 0 °C and 100 °C. With the prediction tests, we showed that both models can cover the entire proposed temperature range and achieve an accuracy of 99.5%, for the classification model, and a mean absolute error of 2.3 °C, in the regression model. We believe that these results show that the strategies implemented can improve the metrological capabilities of this type of sensor.

scholarly journals Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes

2020 ◽  
Author(s):  
Ahmed Abdulaal ◽  
Aatish Patel ◽  
Esmita Charani ◽  
Sarah Denny ◽  
Saleh A Alqahtani ◽  
...  
Keyword(s):  
Deep Learning ◽  
Regression Model ◽  
Predictive Models ◽  
Cox Regression ◽  
Cox Model ◽  
Machine Learning Algorithms ◽  
Prognostic Models ◽  
Ann Model ◽  
Hospital Data ◽  
Cox Regression Model

Abstract Background: Accurately predicting patient outcomes in SARS-CoV-2 could aid patient management and allocation of healthcare resources. There are a variety of methods which can be used to develop prognostic models, ranging from logistic regression and survival analysis to more complex machine learning algorithms and deep learning. Despite several models having been created for SARS-CoV-2, most of these have been found to be highly susceptible to bias. We aimed to develop and compare two separate predictive models for death during admission with SARS-CoV-2. Method: Between March 1 - April 24, 2020, 398 patients were identified with laboratory confirmed SARS-CoV-2 in a London teaching hospital. Data from electronic health records were extracted and used to create two predictive models using: 1) a Cox regression model and 2) an artificial neural network (ANN). Model performance profiles were assessed by validation, discrimination, and calibration. Results: Both the Cox regression and ANN models achieved high accuracy (83.8%, 95% confidence interval (CI): 73.8 - 91.1 and 90.0%, 95% CI: 81.2 - 95.6, respectively). The area under the receiver operator curve (AUROC) for the ANN (92.6%, 95% CI: 91.1 - 94.1) was significantly greater than that of the Cox regression model (86.9%, 95% CI: 85.7 - 88.2), p=0.0136. Both models achieved acceptable calibration with Brier scores of 0.13 and 0.11 for the Cox model and ANN, respectively. Conclusion: We demonstrate an ANN which is non-inferior to a Cox regression model but with potential for further development such that it can learn as new data becomes available. Deep learning techniques are particularly suited to complex datasets with non-linear solutions, which make them appropriate for use in conditions with a paucity of prior knowledge. Accurate prognostic models for SARS-CoV-2 can provide benefits at the patient, departmental and organisational level.

scholarly journals Deep learning generates custom-made logistic regression models for explaining how breast cancer subtypes are classified

2021 ◽  
Author(s):  
Takuma Shibahara ◽  
Chisa Wada ◽  
Yasuho Yamashita ◽  
Kazuhiro Fujita ◽  
Masamichi Sato ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Deep Learning ◽  
Regression Model ◽  
Linear Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Learning Approach ◽  
Logistic Regression Models ◽  
Custom Made

Breast cancer is the most frequently found cancer in women and the one most often subjected to genetic analysis. Nonetheless, it has been causing the largest number of women's cancer-related deaths. PAM50, the intrinsic subtype assay for breast cancer, is beneficial for diagnosis and stratified treatment but does not explain each subtype's mechanism. Nowadays, deep learning can predict the subtypes from genetic information more accurately than conventional statistical methods. However, the previous studies did not directly use deep learning to examine which genes associate with the subtypes. Ours is the first study on a deep-learning approach to reveal the mechanisms embedded in the PAM50-classified subtypes. We developed an explainable deep learning model called a point-wise linear model, which uses a meta-learning approach to generate a custom-made logistic regression model for each sample. Logistic regression is familiar to physicians and medical informatics researchers, and we can use it to analyze which genes are important for subtype prediction. The custom-made logistic regression models generated by the point-wise linear model for each subtype used the specific genes selected in other subtypes compared to the conventional logistic regression model: the overlap ratio is less than twenty percent. And analyzing the point-wise linear model's inner state, we found that the point-wise linear model used genes relevant to the cell cycle-related pathways. The results of this study suggest the potential of our explainable deep learning to play a vital role in cancer treatment.

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