Deep learning data augmentation for Raman spectroscopy cancer tissue classification

AbstractRecently, Raman Spectroscopy (RS) was demonstrated to be a non-destructive way of cancer diagnosis, due to the uniqueness of RS measurements in revealing molecular biochemical changes between cancerous vs. normal tissues and cells. In order to design computational approaches for cancer detection, the quality and quantity of tissue samples for RS are important for accurate prediction. In reality, however, obtaining skin cancer samples is difficult and expensive due to privacy and other constraints. With a small number of samples, the training of the classifier is difficult, and often results in overfitting. Therefore, it is important to have more samples to better train classifiers for accurate cancer tissue classification. To overcome these limitations, this paper presents a novel generative adversarial network based skin cancer tissue classification framework. Specifically, we design a data augmentation module that employs a Generative Adversarial Network (GAN) to generate synthetic RS data resembling the training data classes. The original tissue samples and the generated data are concatenated to train classification modules. Experiments on real-world RS data demonstrate that (1) data augmentation can help improve skin cancer tissue classification accuracy, and (2) generative adversarial network can be used to generate reliable synthetic Raman spectroscopic data.

Download Full-text

Deep Learning Data Augmentation for Raman Spectroscopy Cancer Tissue Classification

10.21203/rs.3.rs-806459/v1 ◽

2021 ◽

Author(s):

Man Wu ◽

Shuwen Wang ◽

Shirui Pan ◽

Andrew C. Terentis ◽

John Strasswimmer ◽

...

Keyword(s):

Raman Spectroscopy ◽

Skin Cancer ◽

Data Augmentation ◽

Cancer Tissue ◽

Tissue Classification ◽

Generative Adversarial Network ◽

Tissue Samples ◽

Normal Tissues ◽

Adversarial Network ◽

Classification Framework

Abstract Recently, Raman Spectroscopy (RS) has demonstrated to be a non-destructive way of cancer diagnosis, due to the uniqueness of RS measurements in revealing molecular biochemical changes between cancerous vs. normal tissues and cells. In order to design computational approaches for cancer detection, the quality and quantity of RS tissues are the basis for accurate prediction. In reality, however, obtaining skin cancer samples is difficult and expensive due to privacy and other constraints. With a small number of samples, the training of the classifier is difficult, and often results in overfitting. Therefore, it is important to have more samples to better train classifiers for accurate cancer tissue classification. To overcome these limitations, this paper presents a novel generative adversarial network based skin cancer tissue classification framework. Specifically, we design a data augmentation module that employs a generative adversarial network to generate synthetic RS samples in different classes. The original tissue samples and the generated data are merged to train classification modules. Experiments on real-world RS data demonstrate that generative adversarial network can be successfully used for data augmentation, in order to train accurate skin cancer tissue classifiers.

Download Full-text

Underwater Acoustic Target Recognition Based on Generative Adversarial Network Data Augmentation

INTER-NOISE and NOISE-CON Congress and Conference Proceedings ◽

10.3397/in-2021-2737 ◽

2021 ◽

Vol 263 (2) ◽

pp. 4558-4564

Author(s):

Minghong Zhang ◽

Xinwei Luo

Keyword(s):

Data Augmentation ◽

Target Recognition ◽

Training Data ◽

Small Samples ◽

Generative Adversarial Network ◽

Data Set ◽

Underwater Acoustic ◽

Adversarial Network ◽

Acoustic Target ◽

The Impact

Underwater acoustic target recognition is an important aspect of underwater acoustic research. In recent years, machine learning has been developed continuously, which is widely and effectively applied in underwater acoustic target recognition. In order to acquire good recognition results and reduce the problem of overfitting, Adequate data sets are essential. However, underwater acoustic samples are relatively rare, which has a certain impact on recognition accuracy. In this paper, in addition of the traditional audio data augmentation method, a new method of data augmentation using generative adversarial network is proposed, which uses generator and discriminator to learn the characteristics of underwater acoustic samples, so as to generate reliable underwater acoustic signals to expand the training data set. The expanded data set is input into the deep neural network, and the transfer learning method is applied to further reduce the impact caused by small samples by fixing part of the pre-trained parameters. The experimental results show that the recognition result of this method is better than the general underwater acoustic recognition method, and the effectiveness of this method is verified.

Download Full-text

Improving Image-Based Plant Disease Classification With Generative Adversarial Network Under Limited Training Set

Frontiers in Plant Science ◽

10.3389/fpls.2020.583438 ◽

2020 ◽

Vol 11 ◽

Author(s):

Luning Bi ◽

Guiping Hu

Keyword(s):

Classification Accuracy ◽

Plant Disease ◽

Data Augmentation ◽

Plant Diseases ◽

Disease Classification ◽

Training Data ◽

Training Dataset ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Overfitting Problem

Traditionally, plant disease recognition has mainly been done visually by human. It is often biased, time-consuming, and laborious. Machine learning methods based on plant leave images have been proposed to improve the disease recognition process. Convolutional neural networks (CNNs) have been adopted and proven to be very effective. Despite the good classification accuracy achieved by CNNs, the issue of limited training data remains. In most cases, the training dataset is often small due to significant effort in data collection and annotation. In this case, CNN methods tend to have the overfitting problem. In this paper, Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is combined with label smoothing regularization (LSR) to improve the prediction accuracy and address the overfitting problem under limited training data. Experiments show that the proposed WGAN-GP enhanced classification method can improve the overall classification accuracy of plant diseases by 24.4% as compared to 20.2% using classic data augmentation and 22% using synthetic samples without LSR.

Download Full-text

Urban Intersection Classification: A Comparative Analysis

Sensors ◽

10.3390/s21186269 ◽

2021 ◽

Vol 21 (18) ◽

pp. 6269

Author(s):

Augusto Luis Ballardini ◽

Álvaro Hernández Saz ◽

Sandra Carrasco Limeros ◽

Javier Lorenzo ◽

Ignacio Parra Alonso ◽

...

Keyword(s):

Ad Hoc ◽

Data Augmentation ◽

Comprehensive Evaluation ◽

Temporal Integration ◽

Individual Performance ◽

Training Data ◽

Real World Data ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Integration Schemes

Understanding the scene in front of a vehicle is crucial for self-driving vehicles and Advanced Driver Assistance Systems, and in urban scenarios, intersection areas are one of the most critical, concentrating between 20% to 25% of road fatalities. This research presents a thorough investigation on the detection and classification of urban intersections as seen from onboard front-facing cameras. Different methodologies aimed at classifying intersection geometries have been assessed to provide a comprehensive evaluation of state-of-the-art techniques based on Deep Neural Network (DNN) approaches, including single-frame approaches and temporal integration schemes. A detailed analysis of most popular datasets previously used for the application together with a comparison with ad hoc recorded sequences revealed that the performances strongly depend on the field of view of the camera rather than other characteristics or temporal-integrating techniques. Due to the scarcity of training data, a new dataset is created by performing data augmentation from real-world data through a Generative Adversarial Network (GAN) to increase generalizability as well as to test the influence of data quality. Despite being in the relatively early stages, mainly due to the lack of intersection datasets oriented to the problem, an extensive experimental activity has been performed to analyze the individual performance of each proposed systems.

Download Full-text

AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network

Sensors ◽

10.3390/s19245479 ◽

2019 ◽

Vol 19 (24) ◽

pp. 5479 ◽

Cited By ~ 1

Author(s):

Maryam Rahnemoonfar ◽

Jimmy Johnson ◽

John Paden

Keyword(s):

Data Augmentation ◽

Synthetic Data ◽

Detection Algorithm ◽

Contour Detection ◽

Training Data ◽

Generative Adversarial Network ◽

Radar Images ◽

Adversarial Network ◽

Radar Imagery

Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. Instead of labeling real images, we can generate synthetic data based on arbitrary labels. In this way, training data can be quickly augmented with additional images. In this research, we evaluated the performance of synthetically generated radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. We also tested the quality of a state-of-the-art contour detection algorithm on synthetic data and different combinations of real and synthetic data. Our experiments show that synthetic radar images generated by generative adversarial network (GAN) can be used in combination with real images for data augmentation and training of deep neural networks. However, the synthetic images generated by GANs cannot be used solely for training a neural network (training on synthetic and testing on real) as they cannot simulate all of the radar characteristics such as noise or Doppler effects. To the best of our knowledge, this is the first work in creating radar sounder imagery based on generative adversarial network.

Download Full-text

Visual and Quantitative Evaluation of Amyloid Brain PET Image Synthesis with Generative Adversarial Network

Applied Sciences ◽

10.3390/app10072628 ◽

2020 ◽

Vol 10 (7) ◽

pp. 2628 ◽

Cited By ~ 2

Author(s):

Hyeon Kang ◽

Jang-Sik Park ◽

Kook Cho ◽

Do-Young Kang

Keyword(s):

Quantitative Evaluation ◽

Data Augmentation ◽

Image Synthesis ◽

Turing Test ◽

Training Data ◽

Generative Adversarial Network ◽

Visual Evaluation ◽

Image Distribution ◽

Adversarial Network ◽

Quantitative Measurements

Conventional data augmentation (DA) techniques, which have been used to improve the performance of predictive models with a lack of balanced training data sets, entail an effort to define the proper repeating operation (e.g., rotation and mirroring) according to the target class distribution. Although DA using generative adversarial network (GAN) has the potential to overcome the disadvantages of conventional DA, there are not enough cases where this technique has been applied to medical images, and in particular, not enough cases where quantitative evaluation was used to determine whether the generated images had enough realism and diversity to be used for DA. In this study, we synthesized 18F-Florbetaben (FBB) images using CGAN. The generated images were evaluated using various measures, and we presented the state of the images and the similarity value of quantitative measurement that can be expected to successfully augment data from generated images for DA. The method includes (1) conditional WGAN-GP to learn the axial image distribution extracted from pre-processed 3D FBB images, (2) pre-trained DenseNet121 and model-agnostic metrics for visual and quantitative measurements of generated image distribution, and (3) a machine learning model for observing improvement in generalization performance by generated dataset. The Visual Turing test showed similarity in the descriptions of typical patterns of amyloid deposition for each of the generated images. However, differences in similarity and classification performance per axial level were observed, which did not agree with the visual evaluation. Experimental results demonstrated that quantitative measurements were able to detect the similarity between two distributions and observe mode collapse better than the Visual Turing test and t-SNE.

Download Full-text

Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network

Computational Intelligence and Neuroscience ◽

10.1155/2020/6490479 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Khaled Almezhghwi ◽

Sertan Serte

Keyword(s):

Blood Cells ◽

Data Augmentation ◽

White Blood Cells ◽

Training Data ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Human Errors ◽

Morphological Variations ◽

Adversarial Network

White blood cells (leukocytes) are a very important component of the blood that forms the immune system, which is responsible for fighting foreign elements. The five types of white blood cells include neutrophils, eosinophils, lymphocytes, monocytes, and basophils, where each type constitutes a different proportion and performs specific functions. Being able to classify and, therefore, count these different constituents is critical for assessing the health of patients and infection risks. Generally, laboratory experiments are used for determining the type of a white blood cell. The staining process and manual evaluation of acquired images under the microscope are tedious and subject to human errors. Moreover, a major challenge is the unavailability of training data that cover the morphological variations of white blood cells so that trained classifiers can generalize well. As such, this paper investigates image transformation operations and generative adversarial networks (GAN) for data augmentation and state-of-the-art deep neural networks (i.e., VGG-16, ResNet, and DenseNet) for the classification of white blood cells into the five types. Furthermore, we explore initializing the DNNs’ weights randomly or using weights pretrained on the CIFAR-100 dataset. In contrast to other works that require advanced image preprocessing and manual feature extraction before classification, our method works directly with the acquired images. The results of extensive experiments show that the proposed method can successfully classify white blood cells. The best DNN model, DenseNet-169, yields a validation accuracy of 98.8%. Particularly, we find that the proposed approach outperforms other methods that rely on sophisticated image processing and manual feature engineering.

Download Full-text

A deep learning segmentation strategy that minimizes the amount of manually annotated images

F1000Research ◽

10.12688/f1000research.52026.1 ◽

2021 ◽

Vol 10 ◽

pp. 256

Author(s):

Thierry Pécot ◽

Alexander Alekseyenko ◽

Kristin Wallace

Keyword(s):

Deep Learning ◽

Data Augmentation ◽

Training Data ◽

Artificial Data ◽

Deep Convolutional Neural Networks ◽

Generative Adversarial Network ◽

Data Set ◽

Segmentation Strategy ◽

Adversarial Network ◽

The Impact

Deep learning has revolutionized the automatic processing of images. While deep convolutional neural networks have demonstrated astonishing segmentation results for many biological objects acquired with microscopy, this technology's good performance relies on large training datasets. In this paper, we present a strategy to minimize the amount of time spent in manually annotating images for segmentation. It involves using an efficient and open source annotation tool, the artificial increase of the training data set with data augmentation, the creation of an artificial data set with a conditional generative adversarial network and the combination of semantic and instance segmentations. We evaluate the impact of each of these approaches for the segmentation of nuclei in 2D widefield images of human precancerous polyp biopsies in order to define an optimal strategy.

Download Full-text

Optimization of Cardiac Magnetic Resonance Synthetic Image Based on Simulated Generative Adversarial Network

Mathematical Problems in Engineering ◽

10.1155/2021/3279563 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Ying Fu ◽

MinXue Gong ◽

Guang Yang ◽

JinRong Hu ◽

Hong Wei ◽

...

Keyword(s):

Cardiac Magnetic Resonance ◽

Magnetic Resonance ◽

Data Augmentation ◽

Small Sample ◽

Training Data ◽

Synthetic Image ◽

Generative Adversarial Network ◽

Data Set ◽

Adversarial Network ◽

Convolution Model

The generative adversarial network (GAN) has advantage to fit data distribution, so it can achieve data augmentation by fitting the real distribution and synthesizing additional training data. In this way, the deep convolution model can also be well trained in the case of using a small sample medical image data set. However, some certain gaps still exist between synthetic images and real images. In order to further narrow those gaps, this paper proposed a method that applies SimGAN on cardiac magnetic resonance synthetic image optimization task. Meanwhile, the improved residual structure is used to deepen the network structure to improve the performance of the optimizer. Lastly, the experiments will show the good result of our data augmentation method based on GAN.

Download Full-text

Intraoral Image Generation by Progressive Growing of Generative Adversarial Network and Evaluation of Generated Image Quality by Dentists

10.21203/rs.3.rs-117942/v1 ◽

2020 ◽

Author(s):

Kazuma Kokomoto ◽

Rena Okawa ◽

Kazuhiko Nakano ◽

Kazunori Nozaki

Keyword(s):

Private Information ◽

Data Augmentation ◽

Wasserstein Distance ◽

Training Data ◽

Image Generation ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Pediatric Dentists ◽

Realistic Images ◽

Clinical Cases

Abstract Dentists need experience with plenty of clinical cases to practice specialized skills. However, the need to protect patients’ private information limits the ability to utilize lots of intraoral images obtained from clinical cases. In this study, since generating realistic images could making utilizing lots of intraoral images possible, intraoral images are generated by using a progressive growing of generative adversarial network. 35,254 intraoral images were used as training data with resolutions of 128×128, 256×256, 512×512, and 1,024×1,024. The results of training datasets with and without data augmentation were compared. The sliced Wasserstein distance (SWD) was calculated to evaluate the generated images. Next, 50 real images and 50 generated images for each resolution were randomly selected and shuffled. Twelve pediatric dentists were asked to observe these images and assess whether each was real or generated. The accuracy of the assessment of the 1,024×1,024 images was significantly higher than that of the other resolutions. In conclusion, generated intraoral images with resolutions of 512×512 or lower were so realistic that the dentists could not distinguish whether they were real or generated. This implies that generated images can be used for dental education or data augmentation for deep learning free from privacy restrictions.

Download Full-text