Metrology and deep learning integrated solution to drive OPC model accuracy improvement

2019 ◽  
Author(s):  
Wei Yuan ◽  
Yifei Lu ◽  
Yuhang Zhao ◽  
Shoumian Chen ◽  
Ming Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Model Accuracy ◽  
Accuracy Improvement

scholarly journals Quality control stress test for deep learning-based diagnostic model in digital pathology

2021 ◽  
Author(s):  
Birgid Schömig-Markiefka ◽  
Alexey Pryalukhin ◽  
Wolfgang Hulla ◽  
Andrey Bychkov ◽  
Junya Fukuoka ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cancer Detection ◽  
Stress Testing ◽  
Computational Analysis ◽  
Stress Test ◽  
Digital Pathology ◽  
Model Performance ◽  
Diagnostic Model ◽  
Model Accuracy ◽  
Diagnostic Models

AbstractDigital pathology provides a possibility for computational analysis of histological slides and automatization of routine pathological tasks. Histological slides are very heterogeneous concerning staining, sections’ thickness, and artifacts arising during tissue processing, cutting, staining, and digitization. In this study, we digitally reproduce major types of artifacts. Using six datasets from four different institutions digitized by different scanner systems, we systematically explore artifacts’ influence on the accuracy of the pre-trained, validated, deep learning-based model for prostate cancer detection in histological slides. We provide evidence that any histological artifact dependent on severity can lead to a substantial loss in model performance. Strategies for the prevention of diagnostic model accuracy losses in the context of artifacts are warranted. Stress-testing of diagnostic models using synthetically generated artifacts might be an essential step during clinical validation of deep learning-based algorithms.

scholarly journals Generalisability through local validation: overcoming barriers due to data disparity in healthcare

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
William Greig Mitchell ◽  
Edward Christopher Dee ◽  
Leo Anthony Celi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Global Health ◽  
Model Validation ◽  
Health Inequity ◽  
Diverse Populations ◽  
Single Centre ◽  
Model Accuracy ◽  
South Korean ◽  
Deep Learning Model

AbstractCho et al. report deep learning model accuracy for tilted myopic disc detection in a South Korean population. Here we explore the importance of generalisability of machine learning (ML) in healthcare, and we emphasise that recurrent underrepresentation of data-poor regions may inadvertently perpetuate global health inequity.Creating meaningful ML systems is contingent on understanding how, when, and why different ML models work in different settings. While we echo the need for the diversification of ML datasets, such a worthy effort would take time and does not obviate uses of presently available datasets if conclusions are validated and re-calibrated for different groups prior to implementation.The importance of external ML model validation on diverse populations should be highlighted where possible – especially for models built with single-centre data.

scholarly journals Deep Learning-Based Beamforming for Millimeter-Wave Systems Using Parametric ReLU Activation Function

2021 ◽  
Author(s):  
Alshimaa Hamdy ◽  
Tarek Abed Soliman ◽  
Mohamed Rihan ◽  
Moawad I. Dessouky
Keyword(s):  
Deep Learning ◽  
Performance Improvement ◽  
Millimeter Wave ◽  
Antenna Arrays ◽  
Activation Function ◽  
Model Accuracy ◽  
Learning Network ◽  
Parametric Rectified Linear Unit ◽  
Complexity Cost ◽  
Deep Learning Network

Abstract Beamforming design is a crucial stage in millimeter-wave systems with massive antenna arrays. We propose a deep learning network for the design of the precoder and combiner in hybrid architectures. The proposed network employs a parametric rectified linear unit (PReLU) activation function which improves model accuracy with almost no complexity cost compared to other functions. The proposed network accepts practical channel estimation input and can be trained to enhance spectral efficiency considering the hardware limitation of the hybrid design. Simulation shows that the proposed network achieves small performance improvement when compared to the same network with the ReLU activation function.

scholarly journals Adversarial Attack Vulnerability of Deep Learning Models for Oncologic Images

2021 ◽  
Author(s):  
Marina Z. Joel ◽  
Sachin Umrao ◽  
Enoch Chang ◽  
Rachel Choi ◽  
Daniel Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Model Performance ◽  
Predictive Ability ◽  
Model Accuracy ◽  
Image Domain ◽  
Attack Model ◽  
Highly Sensitive ◽  
Adversarial Training ◽  
Adversarial Attack ◽  
Magnetic Resonance Imaging Mri

AbstractBackgroundDeep learning (DL) models have shown promise to automate the classification of medical images used for cancer detection. Unfortunately, recent studies have found that DL models are vulnerable to adversarial attacks, which manipulate images with small pixel-level perturbations designed to cause models to misclassify images. There is a need for better understanding of how adversarial attacks impact the predictive ability of DL models in the medical image domain.MethodsWe examined adversarial attacks on DL classification models separately trained on three medical imaging modalities commonly used in oncology: computed tomography (CT), mammography, and magnetic resonance imaging (MRI). We investigated how iterative adversarial training could be employed to increase model robustness against three first-order attack methods.ResultsOn unmodified images, we achieved classification accuracies of 75.4% for CT, 76.4% accuracy for mammogram, and 93.6% for MRI. Under adversarial attack, model accuracy showed a maximum absolute decrease of 49.8% for CT, 52.9% for mammogram, 87.3% for MRI. Adversarial training caused model accuracy on adversarial images to increase by up to 42.9% for CT, 35.7% for mammogram, and 73.2% for MRI.ConclusionOur results indicated that DL models for oncologic images are highly sensitive to adversarial attacks, as visually imperceptible degrees of perturbation are sufficient to deceive the model the majority of the time. Adversarial training mitigated the effect of adversarial attacks on model performance but was less successful against stronger attacks. Our findings provide a useful basis for designing more robust and accurate medical DL models as well as techniques to defend models from adversarial attack.

Effect of input variables on rainfall-runoff modeling using a deep learning method

2021 ◽  
Author(s):  
Kazuki yokoo ◽  
Kei ishida ◽  
Takeyoshi nagasato ◽  
Ali Ercan
Keyword(s):  
Deep Learning ◽  
Air Temperature ◽  
Meteorological Variables ◽  
Learning Method ◽  
Rainfall Runoff ◽  
Model Accuracy ◽  
Runoff Modeling ◽  
Input Variables ◽  
Rainfall Runoff Model ◽  
Runoff Model

<p>In recent years, deep learning has been applied to various issues in natural science, including hydrology. These application results show its high applicability. There are some studies that performed rainfall-runoff modeling by means of a deep learning method, LSTM (Long Short-Term Memory). LSTM is a kind of RNN (Recurrent Neural Networks) that is suitable for modeling time series data with long-term dependence. These studies showed the capability of LSTM for rainfall-runoff modeling. However, there are few studies that investigate the effects of input variables on the estimation accuracy. Therefore, this study, investigated the effects of the selection of input variables on the accuracy of a rainfall-runoff model by means of LSTM. As the study watershed, this study selected a snow-dominated watershed, the Ishikari River basin, which is in the Hokkaido region of Japan. The flow discharge was obtained at a gauging station near the outlet of the river as the target data. For the input data to the model, Meteorological variables were obtained from an atmospheric reanalysis dataset, ERA5, in addition to the gridded precipitation dataset. The selected meteorological variables were air temperature, evaporation, longwave radiation, shortwave radiation, and mean sea level pressure. Then, the rainfall-runoff model was trained with several combinations of the input variables. After the training, the model accuracy was compared among the combinations. The use of meteorological variables in addition to precipitation and air temperature as input improved the model accuracy. In some cases, however, the model accuracy was worsened by using more variables as input. The results indicate the importance to select adequate variables as input for rainfall-runoff modeling by LSTM.</p>

scholarly journals Accuracy Improvement in DOA Estimation with Deep Learning

2021 ◽  
Author(s):  
Yuya KASE ◽  
Toshihiko NISHIMURA ◽  
Takeo OHGANE ◽  
Yasutaka OGAWA ◽  
Takanori SATO ◽  
...  
Keyword(s):  
Deep Learning ◽  
Doa Estimation ◽  
Accuracy Improvement

scholarly journals A Survey of Deep Learning Solutions for Anomaly Detection in Surveillance Videos

2021 ◽  
Vol 10 (5) ◽  
Author(s):  
John Gatara Munyua ◽  
Geoffrey Mariga Wambugu ◽  
Stephen Thiiru Njenga
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Model Accuracy ◽  
Surveillance Videos ◽  
Emerging Trends ◽  
Learning Technique ◽  
Learning Reinforcement ◽  
Security Incidents ◽  
Computing Approach ◽  
Continual Learning

Deep learning has proven to be a landmark computing approach to the computer vision domain. Hence, it has been widely applied to solve complex cognitive tasks like the detection of anomalies in surveillance videos. Anomaly detection in this case is the identification of abnormal events in the surveillance videos which can be deemed as security incidents or threats. Deep learning solutions for anomaly detection has outperformed other traditional machine learning solutions. This review attempts to provide holistic benchmarking of the published deep learning solutions for videos anomaly detection since 2016. The paper identifies, the learning technique, datasets used and the overall model accuracy. Reviewed papers were organised into five deep learning methods namely; autoencoders, continual learning, transfer learning, reinforcement learning and ensemble learning. Current and emerging trends are discussed as well.

scholarly journals Identifying individuals with recent COVID-19 through voice classification using deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pichatorn Suppakitjanusant ◽  
Somnuek Sungkanuparph ◽  
Thananya Wongsinin ◽  
Sirapong Virapongsiri ◽  
Nittaya Kasemkosin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Performance ◽  
The Other ◽  
Healthy Individuals ◽  
Model Accuracy ◽  
Subtle Change ◽  
Classification Of Images ◽  
A Prospective Study ◽  
Voice Classification

AbstractRecently deep learning has attained a breakthrough in model accuracy for the classification of images due mainly to convolutional neural networks. In the present study, we attempted to investigate the presence of subclinical voice feature alteration in COVID-19 patients after the recent resolution of disease using deep learning. The study was a prospective study of 76 post COVID-19 patients and 40 healthy individuals. The diagnoses of post COVID-19 patients were based on more than the eighth week after onset of symptoms. Voice samples of an ‘ah’ sound, coughing sound and a polysyllabic sentence were collected and preprocessed to log-mel spectrogram. Transfer learning using the VGG19 pre-trained convolutional neural network was performed with all voice samples. The performance of the model using the polysyllabic sentence yielded the highest classification performance of all models. The coughing sound produced the lowest classification performance while the ability of the monosyllabic ‘ah’ sound to predict the recent COVID-19 fell between the other two vocalizations. The model using the polysyllabic sentence achieved 85% accuracy, 89% sensitivity, and 77% specificity. In conclusion, deep learning is able to detect the subtle change in voice features of COVID-19 patients after recent resolution of the disease.

Sign in / Sign up

Export Citation Format

Share Document