Dataset Information

Synthetic image augmentation with generative adversarial network for enhanced performance in protein classification.

ABSTRACT: Proteins are complex macromolecules accountable for the biological processes in the cell. In biomedical research, the images of protein are extensively used in medicine. The rate at which these images are produced makes it difficult to evaluate them manually and hence there exists a need to automate the system. The quality of images is still a major issue. In this paper, we present the use of different image enhancement techniques that improves the contrast of these images. Besides the quality of images, the challenge of gathering such datasets in the field of medicine persists. We use generative adversarial networks for generating synthetic samples to ameliorate the results of CNN. The performance of the synthetic data augmentation was compared with the classic data augmentation on the classification task, an increase of 2.7% in Macro F1 and 2.64% in Micro F1 score was observed. Our best results were obtained by the pretrained Inception V4 model that gave a fivefold cross-validated macro F1 of 0.603. The achieved results are contrasted with the existing work and comparisons show that the proposed method outperformed.

SUBMITTER: Verma R

PROVIDER: S-EPMC7438425 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Synthetic image augmentation with generative adversarial network for enhanced performance in protein classification.

Verma Rohit R Mehrotra Raj R Rane Chinmay C Tiwari Ritu R Agariya Arun Kumar AK

Biomedical engineering letters 20200713 3

Proteins are complex macromolecules accountable for the biological processes in the cell. In biomedical research, the images of protein are extensively used in medicine. The rate at which these images are produced makes it difficult to evaluate them manually and hence there exists a need to automate the system. The quality of images is still a major issue. In this paper, we present the use of different image enhancement techniques that improves the contrast of these images. Besides the quality o ...[more]

PMID: 32850179

Similar Datasets

Project description:This study proposed an end-to-end unsupervised medical fusion generative adversarial network, MedFusionGAN, to fuse computed tomography (CT) and high-resolution isotropic 3D T1-Gd Magnetic resonance imaging (MRI) image sequences to generate an image with CT bone structure and MRI soft tissue contrast to improve target delineation and to reduce the radiotherapy planning time. We used a publicly available multicenter medical dataset (GLIS-RT, 230 patients) from the Cancer Imaging Archive. To improve the models generalization, we consider different imaging protocols and patients with various brain tumor types, including metastases. The proposed MedFusionGAN consisted of one generator network and one discriminator network trained in an adversarial scenario. Content, style, and L1 losses were used for training the generator to preserve the texture and structure information of the MRI and CT images. The MedFusionGAN successfully generates fused images with MRI soft-tissue and CT bone contrast. The results of the MedFusionGAN were quantitatively and qualitatively compared with seven traditional and eight deep learning (DL) state-of-the-art methods. Qualitatively, our method fused the source images with the highest spatial resolution without adding the image artifacts. We reported nine quantitative metrics to quantify the preservation of structural similarity, contrast, distortion level, and image edges in fused images. Our method outperformed both traditional and DL methods on six out of nine metrics. And it got the second performance rank for three and two quantitative metrics when compared with traditional and DL methods, respectively. To compare soft-tissue contrast, intensity profile along tumor and tumor contours of the fusion methods were evaluated. MedFusionGAN provides a more consistent, better intensity profile, and a better segmentation performance. The proposed end-to-end unsupervised method successfully fused MRI and CT images. The fused image could improve targets and OARs delineation, which is an important aspect of radiotherapy treatment planning.

Project description:Purpose The identification of abnormalities that are relatively rare within otherwise normal anatomy is a major challenge for deep learning in the semantic segmentation of medical images. The small number of samples of the minority classes in the training data makes the learning of optimal classification challenging, while the more frequently occurring samples of the majority class hamper the generalization of the classification boundary between infrequently occurring target objects and classes. In this paper, we developed a novel generative multi-adversarial network, called Ensemble-GAN, for mitigating this class imbalance problem in the semantic segmentation of abdominal images.Method The Ensemble-GAN framework is composed of a single-generator and a multi-discriminator variant for handling the class imbalance problem to provide a better generalization than existing approaches. The ensemble model aggregates the estimates of multiple models by training from different initializations and losses from various subsets of the training data. The single generator network analyzes the input image as a condition to predict a corresponding semantic segmentation image by use of feedback from the ensemble of discriminator networks. To evaluate the framework, we trained our framework on two public datasets, with different imbalance ratios and imaging modalities: the Chaos 2019 and the LiTS 2017.Result In terms of the F1 score, the accuracies of the semantic segmentation of healthy spleen, liver, and left and right kidneys were 0.93, 0.96, 0.90 and 0.94, respectively. The overall F1 scores for simultaneous segmentation of the lesions and liver were 0.83 and 0.94, respectively.Conclusion The proposed Ensemble-GAN framework demonstrated outstanding performance in the semantic segmentation of medical images in comparison with other approaches on popular abdominal imaging benchmarks. The Ensemble-GAN has the potential to segment abdominal images more accurately than human experts.

Dataset Information

Synthetic image augmentation with generative adversarial network for enhanced performance in protein classification.

Publications

Synthetic image augmentation with generative adversarial network for enhanced performance in protein classification.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets