Introduction:
Recent research on Generative Adversarial Networks (GANs) in the biomedical field has proven the effectiveness in generating synthetic images of different modalities. Ultrasound imaging is one of the primary imaging modalities for diagnosis in the medical domain. In this paper, we present an empirical analysis of the state-of-the-art Deep Convolutional Generative Adversarial Network (DCGAN) for generating synthetic ultrasound images.
Aims:
This work aims to explore the utilization of deep convolutional generative adversarial networks for the synthesis of ultrasound images and to leverage its capabilities.
Background:
Ultrasound imaging plays a vital role in healthcare for timely diagnosis and treatment. Increasing interest in automated medical image analysis for precise diagnosis has expanded the demand for a large number of ultrasound images. Generative adversarial networks have been proven beneficial for increasing the size of data by generating synthetic images.
Objective:
Our main purpose in generating synthetic ultrasound images is to produce a sufficient amount of ultrasound images with varying representations of a disease.
Methods:
DCGAN has been used to generate synthetic ultrasound images. It is trained on two ultrasound image datasets, namely, the common carotid artery dataset and nerve dataset, which are publicly available on Signal Processing Lab and Kaggle, respectively.
Results:
Results show that good quality synthetic ultrasound images are generated within 100 epochs of training of DCGAN. The quality of synthetic ultrasound images is evaluated using Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). We have also presented some visual representations of the slices of generated images for qualitative comparison.
Conclusion:
Our empirical analysis reveals that synthetic ultrasound image generation using DCGAN is an efficient approach.
Other:
In future work, we plan to compare the quality of images generated through other adversarial methods such as conditional GAN, progressive GAN.
The Hadoop Distributed File System (HDFS) is a representative cloud storage platform having scalable, reliable and low-cost storage capability. It is designed to handle large files. Hence, it suffers performance penalty while handling a huge number of small files. Further, it does not consider the correlation between the files to provide prefetching mechanism that is useful to improve access efficiency. In this paper, we propose a novel approach to handle small files in HDFS. The proposed approach combines the correlated files into one single file to reduce the metadata storage on Namenode. We integrate the prefetching and caching mechanisms in the proposed approach to improve access efficiency of small files. Moreover, we analyze the performance of the proposed approach considering file sizes in range 32KB-4096KB. The results show that the proposed approach reduces the metadata storage compared to HDFS.
Index Terms-Hadoop, HDFS, small files, file correlation, prefetching. 978-1-4799-8047-5/15/$31.00 c 2015 IEEE
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