This research, presented at the Oklahoma State University Undergraduate Summer Student Research Expo, is the work of a visiting author from Bilkent University (Turkey).Research Experiences for Undergraduates in Computer ScienceComputer Scienc

Kiyma, Sumeyye Sena

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Exploring Genetic Algorithm as an Image Synthesizer for Cases with Limited Training SamplesSumeyye Sena Kiyma1; Rittika Shamsuddin21Bilkent University (Turkey), 2Oklahoma State UniversitySince we want to combine pattern exploration with minimal training and controlled variation, webegin with the simplest dataset e.g., MNIST dataset, to develop our model as an expert will not beneeded to evaluate the synthesized data (unlike most healthcare data). Once the synthetic digits aresatisfactory the next part will be based on CIFAR-10 dataset, then healthcare images and finallyother types of healthcare data such as time series data. Another reason to use the MNIST dataset is tocompare the synthesizer with other models that have been built previously.So far, we have used 2 different methods based on genetic algorithm. You can see the pseudocodefor these methods in Figure 2 and Figure 3. Method 1 uses the genetic algorithm with one individualwhere the individual is considered as the “to be synthesized image”. This individual is being mutatedin each iteration and if the mutated individual is more fit than the previous one it is replaced tobecome the individual. This goes on for each iteration until a certain number of generations isreached.In Method 2 there is a population with more than one member, and the offspring are created by bothmutation and crossover between the best fit parents. This method is more open to allow variationsince Method 1 is only pushing one member to become more fit whereas in Method 2 the membersof the population can crossover and mutate for variance.The fitness value that is used for both methods is calculated by the objective function and indicateswhether the synthetic image meets the criteria and is a part of the class that is to be generated byusing distance metrics. The equations for the objective function and the fitness value can be seenbelow.IntroductionGuided Evolutionary Synthesizer (GES) has been used to synthesize time series healthcare data forResidual Network (ResNet) models and it is shown that data synthesized by GES worked better thanperturbated data for training models (see Table 3). Table 1 and Table 2 also shows that how theResNet results improved with synthetic data. However, this model was not used with images andwas not compared to other synthesizers [3]. Virtual Patient Model (VPM) was also built for datasynthesis in healthcare with genetic algorithm, yet it was not comparable to other synthesizers [1].As of now, Generative Adversarial Networks (GANs) and its variations dominate image synthesis inthe field of computer science. GANs are synthesizers that constitute two major parts: generator anddiscriminator. The generator synthesizes new data that is sent to the discriminator. The discriminatortries to discriminate between “real” and “generated” data by a classifier. By using back propagation,the generator tries to fool the discriminator [2]. This adversarial training between discriminator andgenerator makes it difficult to converge and the volume of the training data is immense. Ourresearch is focused on synthesizing data with smaller datasets. On the other hand, the objectivefunction of GANs must be differentiable for backpropagation which cannot be achieved in everystudy. The purpose of this study is finding a method for data synthesis with smaller datasets andwhere the objective function is not limited to be differentiable.Related WorkThe synthetic images for each class for each method can be seen in Figure 5 and Figure 6. Whenthese images are classified by a machine learning model it is seen that there is 80% true positiverate with Method 1, and 78.13% with Method 2. You can see that not all images have the samequality of matching the class, however, almost all of them represents their class from a human view.Also, it must not be forgotten that all these images for each class are generated from the same sevenimages from the seed-data. The main purpose of this research is to explore and determine whetherGA is capable of pattern exploration while allowing researchers to control the amount of variationsseen in final synthesized output. At this moment of the research, we do not have a comparisonmethod with GAN, however, we do see some variations in our experimental output. We plan tocontinue our experiments.ResultsSynthetic data is crucial in various fields. Currently, data synthesis is widely being used in patternexploration in image data. In other fields, such as healthcare, data synthesis can be leveraged toeither increase data volume and/or to improve privacy and data security. In healthcare fieldspecifically, it can intrude privacy, and/or be expensive to collect data from the patients. This oftenresults in lack of data for training state-of-the-art machine learning based programs [1]. Thus,synthetic data can play a huge role in healthcare-based applications, and yet, there is limited data-synthesis work done on healthcare non-image data where only limited data is available.In this research we want to find a method for data synthesis, such that data can be generated fromminimal data, with limited hyperparameter changes between different data types, and such that theuser has control over the variation included in the synthetic data (often medical data allows onlyslight deviations, for example the ECG data that can be seen in Figure 1). As such, we focus onsynthesizing data from a small dataset by using the Genetic Algorithm (GA). A genetic algorithm isbased on Charles Darwin’s theory of natural evolution. In this algorithm the fittest individualssurvive and produce offspring of the next generation [4]. For this study, the synthetic data is chosento be the best fit individual at the end of the process.MethodologyFigure 5. Synthesized images with Method 1.[1] R. Shamsuddin, B. M. Maweu, M. Li and a. B. Prabhakaran, "Virtual Patient Model: An Approach for Generating," IEEE International Conference on Healthcare Informatics, pp. 208-218, 2018. [2] Z. Pan, W. Yu, X. Yi, A. Khan, F. Yuan and Y. Zheng, "Recent progress on generative adversarial networks (GANs): A survey," IEEE Access, vol. 7, pp. 36322-36333, 2019. [3] B. M. Maweu, R. Shamsuddin, S. Dakshit and B. Prabhakaran, "Generating Healthcare Time Series Data for Improving Diagnostic Accuracy of Deep Neural Networks," IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, vol. 70, pp. 1-15, 2021. [4] S. Katoch, S. S. Chauhan and V. Kumar, "A review on genetic algorithm: past, present, and future," Multimedia Tools and Applications, vol. 80, p. 8091–8126, 2021.ReferencesThe purpose of this research is to synthesize healthcare data from a small dataset for differentmachine learning models that would be used for training. This part of the research is based on digitimages that are more common and can be analyzed easily without a healthcare expert. The syntheticimages so far have been successful to an extend. However, we will keep experimenting for betterresults and when satisfied results are observed the research will proceed in healthcare data.The experiments so far were based on Method 1 and Method 2, and the results were as expected. Theresearch will continue more dominantly with Method 2; however, a computational expert andstatistical inferences will be combined with the objective function to get class specific results. Withthese extensions, which can be seen in Figure 7, it is believed that the quality of the images will beincreased together with the variation.Conclusions & Future WorkStart with one individualFor each generation:Compute fitness of the individualMutate the individual Compute fitness of the mutated individualReplace individual with mutated individual if mutation increased fitnessStop when (max number of generations is reached)Figure 2. Pseudocode of Method 1.Figure 6. Synthesized images with Method 2.𝑔 = ෍𝑖=1𝑘𝑑(𝑥𝑟𝑒𝑎𝑙𝑖 − 𝑥𝑠𝑦𝑛𝑡ℎ𝑒𝑠𝑖𝑧𝑒𝑑)𝑓 =1𝑔𝑔: 𝑜𝑏𝑗𝑒𝑐𝑡𝑖𝑣𝑒 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛𝑑: 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛𝑓: 𝑓𝑖𝑡𝑛𝑒𝑠𝑠 𝑣𝑎𝑙𝑢𝑒𝑘: 𝑛𝑢𝑚𝑏𝑒𝑟 𝑒𝑙𝑒𝑚𝑒𝑛𝑡𝑠 𝑖𝑛 𝑠𝑒𝑒𝑑 − 𝑑𝑎𝑡𝑎𝑥𝑟𝑒𝑎𝑙𝑖 : 𝑖𝑡ℎ 𝑖𝑚𝑎𝑔𝑒 𝑖𝑛 𝑠𝑒𝑒𝑑 − 𝑑𝑎𝑡𝑎𝑥𝑠𝑦𝑛𝑡ℎ𝑒𝑠𝑖𝑧𝑒𝑑: 𝑡𝑜 𝑏𝑒 𝑠𝑦𝑛𝑡ℎ𝑒𝑠𝑖𝑧𝑒𝑑 𝑑𝑎𝑡𝑎Table 1. Top 5 ECG trained ResNet models sorted to show best balance on specificity and sensitivity for NSR & PVC classes [3].Table 3. Classification performance for ResNet models trained with GES synthetic data with[No Reg, Reg1, Reg2] and models trained with data perturbation synthetic data [3].Figure 4. Convergence: Fitness value vs generation number for a) Method 1, b) Method 2.Figure 1. Overlay plot of ECG target class NSR that shows DTW alignment of real and GES synthetic data [3].Table 2. Performance metrics: Models trained with real and GES synthetic data [3].PopulationMutation CrossoverApplication OperatorsFitness ValueGAStatistical/Computational InferenceComputational ExpertFigure 7. Future work diagram.Start with n individuals as the populationFor each generation:Compute fitness of the populationDo crossover over the populationMutate the individuals Replace individuals with mutated individuals if mutation increased fitnessStop when (max number of generations is reached)Figure 3. Pseudocode of Method 2.

Exploring genetic algorithm as an image synthesizer for cases with limited training samples

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Exploring genetic algorithm as an image synthesizer for cases with limited training samples

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