Deep Learning based Gastric Lesion Classification System using Data Augmentation

PurposeA core challenge in background subtraction (BGS) is handling videos with sudden illumination changes in consecutive frames. In our pilot study published in, Sakkos:SKIMA 2019, we tackle the problem from a data point-of-view using data augmentation. Our method performs data augmentation that not only creates endless data on the fly but also features semantic transformations of illumination which enhance the generalisation of the model.Design/methodology/approachIn our pilot study published in SKIMA 2019, the proposed framework successfully simulates flashes and shadows by applying the Euclidean distance transform over a binary mask generated randomly. In this paper, we further enhance the data augmentation framework by proposing new variations in image appearance both locally and globally.FindingsExperimental results demonstrate the contribution of the synthetics in the ability of the models to perform BGS even when significant illumination changes take place.Originality/valueSuch data augmentation allows us to effectively train an illumination-invariant deep learning model for BGS. We further propose a post-processing method that removes noise from the output binary map of segmentation, resulting in a cleaner, more accurate segmentation map that can generalise to multiple scenes of different conditions. We show that it is possible to train deep learning models even with very limited training samples. The source code of the project is made publicly available at https://github.com/dksakkos/illumination_augmentation

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Imbalanced Toxic Comments Classification Using Data Augmentation and Deep Learning

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) ◽

10.1109/icmla.2018.00141 ◽

2018 ◽

Cited By ~ 6

Author(s):

Mai Ibrahim ◽

Marwan Torki ◽

Nagwa El-Makky

Keyword(s):

Deep Learning ◽

Data Augmentation ◽

Using Data

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aiai at the FinSBD-3 task: Structure Boundary Detection of Noisy Financial Texts in English and French Using Data Augmentation and Hybrid Deep Learning Model

Companion Proceedings of the Web Conference 2021 ◽

10.1145/3442442.3451380 ◽

2021 ◽

Author(s):

Ke Tian ◽

Hua Chen

Keyword(s):

Deep Learning ◽

Data Augmentation ◽

Boundary Detection ◽

Learning Model ◽

Task Structure ◽

Structure Boundary ◽

Using Data ◽

Deep Learning Model

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Deep Learning for Topmost Roller Chain Detection Using Data Augmentation

2019 4th International Conference on Mechanical, Control and Computer Engineering (ICMCCE) ◽

10.1109/icmcce48743.2019.00106 ◽

2019 ◽

Author(s):

Yulin Wang ◽

Yijun Zhou ◽

Chen Luo

Keyword(s):

Deep Learning ◽

Data Augmentation ◽

Roller Chain ◽

Using Data

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Face Recognition via Deep Learning Using Data Augmentation Based on Orthogonal Experiments

Electronics ◽

10.3390/electronics8101088 ◽

2019 ◽

Vol 8 (10) ◽

pp. 1088 ◽

Cited By ~ 4

Author(s):

Zhao Pei ◽

Hang Xu ◽

Yanning Zhang ◽

Min Guo ◽

Yee-Hong Yang

Keyword(s):

Deep Learning ◽

Face Recognition ◽

Large Scale ◽

Data Augmentation ◽

Small Sample ◽

Geometric Transformation ◽

Image Brightness ◽

Class Attendance ◽

Orthogonal Experiments ◽

Using Data

Class attendance is an important means in the management of university students. Using face recognition is one of the most effective techniques for taking daily class attendance. Recently, many face recognition algorithms via deep learning have achieved promising results with large-scale labeled samples. However, due to the difficulties of collecting samples, face recognition using convolutional neural networks (CNNs) for daily attendance taking remains a challenging problem. Data augmentation can enlarge the samples and has been applied to the small sample learning. In this paper, we address this problem using data augmentation through geometric transformation, image brightness changes, and the application of different filter operations. In addition, we determine the best data augmentation method based on orthogonal experiments. Finally, the performance of our attendance method is demonstrated in a real class. Compared with PCA and LBPH methods with data augmentation and VGG-16 network, the accuracy of our proposed method can achieve 86.3%. Additionally, after a period of collecting more data, the accuracy improves to 98.1%.

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Deep learning based model for Defect Detection of Mono-Crystalline-Si Solar PV Module Cells in Electroluminescence Images Using Data Augmentation

2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) ◽

10.1109/appeec45492.2019.8994713 ◽

2019 ◽

Author(s):

Wuqin Tang ◽

Qiang Yang ◽

Wenjun Yan

Keyword(s):

Deep Learning ◽

Defect Detection ◽

Data Augmentation ◽

Solar Pv ◽

Pv Module ◽

Using Data

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Medical Augmentation (Med-Aug) for Optimal Data Augmentation in Medical Deep Learning Networks

Sensors ◽

10.3390/s21217018 ◽

2021 ◽

Vol 21 (21) ◽

pp. 7018

Author(s):

Justin Lo ◽

Jillian Cardinell ◽

Alejo Costanzo ◽

Dafna Sussman

Keyword(s):

Deep Learning ◽

High Performance ◽

Data Augmentation ◽

Search Algorithm ◽

High Dimensional ◽

Learning Networks ◽

Imaging Data ◽

Covariance Matrix Adaptation ◽

Using Data ◽

Adaptation Evolution

Deep learning (DL) algorithms have become an increasingly popular choice for image classification and segmentation tasks; however, their range of applications can be limited. Their limitation stems from them requiring ample data to achieve high performance and adequate generalizability. In the case of clinical imaging data, images are not always available in large quantities. This issue can be alleviated by using data augmentation (DA) techniques. The choice of DA is important because poor selection can possibly hinder the performance of a DL algorithm. We propose a DA policy search algorithm that offers an extended set of transformations that accommodate the variations in biomedical imaging datasets. The algorithm makes use of the efficient and high-dimensional optimizer Bi-Population Covariance Matrix Adaptation Evolution Strategy (BIPOP-CMA-ES) and returns an optimal DA policy based on any input imaging dataset and a DL algorithm. Our proposed algorithm, Medical Augmentation (Med-Aug), can be implemented by other researchers in related medical DL applications to improve their model’s performance. Furthermore, we present our found optimal DA policies for a variety of medical datasets and popular segmentation networks for other researchers to use in related tasks.

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