![]() We achieved a misclassification rate of 12.3% for the generated images, while the expert evaluation showed the true recognition rate (TRR) of 80.00% (for four-cell images), 86.8% (for two-cell images), and 96.2% (for one-cell images). The proposed algorithm used generative adversarial network (GAN) to generate one-, two-, and four-cell stage images. We use human embryo images obtained during cell development processes for training a deep neural network (DNN). Our focus was not only to generate the generic image of a cell such, but to make sure that it has all necessary attributes of a real cell image to provide a fully realistic synthetic version. We propose a method for generating the synthetic images of human embryo cells that could later be used for classification, analysis, and training, thus resulting in the creation of new synthetic image datasets for research areas lacking real-world data. For example, when training the model with standard augmentation techniques, performance of segmentation of the kidneys on out-of-distribution non-contrast images was dramatically lower than for in-distribution data (Dice score of 0.09 vs. We show that in several CT segmentation tasks performance is improved significantly, especially in out-of-distribution (noncontrast CT) data. We refer to these 2 separate datasets as the in-distribution and out-of-distribution datasets, respectively. We further evaluated the U-Net segmentation performance on two separate datasets: The original contrast CT dataset on which segmentations were created and a second dataset from a different hospital containing only non-contrast CTs. We compared the segmentation performance of a U-Net trained on the original dataset compared to a U-Net trained on the combined dataset of original data and synthetic non-contrast images. We then used the trained CycleGAN to augment our training using these synthetic non-contrast images. Using a large image database we trained a CycleGAN to transform contrast CT images into non-contrast images. We evaluate the use of CycleGAN for data augmentation in CT segmentation tasks. Generative adversarial networks offer a novel method for data augmentation. Standard data augmentation is a method to increase generalizability and is routinely performed. To achieve generalizable deep learning models large amounts of data are needed. The results demonstrate accurate performance and good generalization for all kinds of anomalies, specifically for texture-shaped images where the method reaches an average accuracy of 97.2% (85.4% with an additional zero false negative constraint).Ībstract Labeled medical imaging data is scarce and expensive to generate. The proposed method is evaluated on industrial and medical images, including cases with balanced datasets and others with as few as 30 abnormal images. Based on a threshold set with a business quality constraint, the input image is then flagged as normal or not. After an input image has been reconstructed by the normal generator, an anomaly score describes the differences between the input and reconstructed images. To the best of our knowledge, this is the first time that Cycle-GANs have been studied for this purpose. To address this challenge, the proposed method utilizes Cycle-Generative Adversarial Networks (Cycle-GANs) for abnormal-to-normal translation. Each of these tasks could help the entire model to learn with higher precision than a single normal to normal reconstruction. ![]() Indeed, the model would be able to identify its weaknesses by better learning how to transform an abnormal (or normal) image into a normal (or abnormal) image. However, the information contained in the abnormal data is also valuable for this reconstruction. Such methods only rely on normal images during training, which are devoted to be reconstructed through an autoencoder architecture for instance. The AD is often formulated as an unsupervised task motivated by the frequent imbalanced nature of the datasets, as well as the challenge of capturing the entirety of the abnormal class. This method leverages the theoretical strengths of unsupervised learning and the data availability of both normal and abnormal classes. In this study, a new Anomaly Detection (AD) approach for real-world images is proposed.
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