Cycle GAN-Based Data Augmentation For Multi-Organ Detection In CT Images Via Yolo

Hammami, Maryam; Friboulet, Denis; Kéchichian, Razmig

doi:10.1109/icip40778.2020.9191127

Cited by 42 publications

(18 citation statements)

References 18 publications

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“…Then, the way in which the generated and real images are used in different phases needs to be emphasized again. In 2020, Maryam Hammami et al proposed a CycleGAN and YOLO combined model for data augmentation and used generated data and real data to train a YOLO detector, in which generated data and real data are simultaneously input into YOLO for training [61]. In our study, the detector was trained using only generated images in the pre-training phase and only real images in the transfer learning phase, which is a typically network-based deep transfer learning method.…”

Section: Discussionmentioning

confidence: 99%

NDFTC: A New Detection Framework of Tropical Cyclones from Meteorological Satellite Images with Deep Transfer Learning

Pang

Xie

et al. 2021

Remote Sensing

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Accurate detection of tropical cyclones (TCs) is important to prevent and mitigate natural disasters associated with TCs. Deep transfer learning methods have advantages in detection tasks, because they can further improve the stability and accuracy of the detection model. Therefore, on the basis of deep transfer learning, we propose a new detection framework of tropical cyclones (NDFTC) from meteorological satellite images by combining the deep convolutional generative adversarial networks (DCGAN) and You Only Look Once (YOLO) v3 model. The algorithm process of NDFTC consists of three major steps: data augmentation, a pre-training phase, and transfer learning. First, to improve the utilization of finite data, DCGAN is used as the data augmentation method to generate images simulated to TCs. Second, to extract the salient characteristics of TCs, the generated images obtained from DCGAN are inputted into the detection model YOLOv3 in the pre-training phase. Furthermore, based on the network-based deep transfer learning method, we train the detection model with real images of TCs and its initial weights are transferred from the YOLOv3 trained with generated images. Training with real images helps to extract universal characteristics of TCs and using transferred weights as initial weights can improve the stability and accuracy of the model. The experimental results show that the NDFTC has a better performance, with an accuracy (ACC) of 97.78% and average precision (AP) of 81.39%, in comparison to the YOLOv3, with an ACC of 93.96% and AP of 80.64%.

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Section: Discussionmentioning

confidence: 99%

NDFTC: A New Detection Framework of Tropical Cyclones from Meteorological Satellite Images with Deep Transfer Learning

Pang

Xie

et al. 2021

Remote Sensing

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“…Compared to traditional augmentation methods, mostly based on image processing techniques, generative models such as GANs can capture the semantic features of images used for training and generate similar but diferent images to enhance the quantity and diversity of training data. Such capability of GANs has driven its usage in image augmentation for various computer vision tasks, including classifcation [23,24], object detection [25,26], and semantic/image segmentation [27][28][29][30]. Tese prior studies have validated the efectiveness of GANs as an image augmentation technique.…”

Section: Data Augmentation Techniquesmentioning

confidence: 99%

Conditional TransGAN‐Based Data Augmentation for PCB Electronic Component Inspection

Wang

Huang

et al. 2023

Computational Intelligence and Neuroscience

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Automatic recognition and positioning of electronic components on PCBs can enhance quality inspection efficiency for electronic products during manufacturing. Efficient PCB inspection requires identification and classification of PCB components as well as defects for better quality assurance. The small size of the electronic component and PCB defect targets means that there are fewer feature areas for the neural network to detect, and the complex grain backgrounds of both datasets can cause significant interference, making the target detection task challenging. Meanwhile, the detection performance of deep learning models is significantly impacted due to the lack of samples. In this paper, we propose conditional TransGAN (cTransGAN), a generative model for data augmentation, which enhances the quantity and diversity of the original training set and further improves the accuracy of PCB electronic component recognition. The design of cTransGAN brings together the merits of both conditional GAN and TransGAN, allowing a trained model to generate high-quality synthetic images conditioned on the class embeddings. To validate the proposed method, we conduct extensive experiments on two datasets, including a self-developed dataset for PCB component detection and an existing dataset for PCB defect detection. Also, we have evaluated three existing object detection algorithms, including Faster R-CNN ResNet101, YOLO V3 DarkNet-53, and SCNet ResNet101, and each is validated under four experimental settings to form an ablation study. Results demonstrate that the proposed cTransGAN can effectively enhance the quality and diversity of the training set, leading to superior performance on both tasks. We have open-sourced the project to facilitate further studies.

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“…The self-attention MADGAN could detect AD at an early stage, with an area under the curve of 0.727, and AD at a late stage with AUC 0.894, whereas it achieved AUC 0.921 for brain metastases detection on T1c scans. Moreover, Maryam Hammami et al [42] designed a combined Cycle GAN and YOLO method for CT data augmentation. The experimental findings showed that detection was speedy and accurate, with an average distance of 7.95 ± 6.2 mm, which was particularly superior to detection without being augmented.…”

Section: Related Workmentioning

confidence: 99%

Symmetry GAN Detection Network: An Automatic One-Stage High-Accuracy Detection Network for Various Types of Lesions on CT Images

Zhang

et al. 2022

Symmetry

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Computed tomography (CT) is the first modern slice-imaging modality. Recent years have witnessed its widespread application and improvement in detecting and diagnosing related lesions. Nonetheless, there are several difficulties in detecting lesions in CT images: (1) image quality degrades as the radiation dose is reduced to decrease radiational injury to the human body; (2) image quality is frequently hampered by noise interference; (3) because of the complicated circumstances of diseased tissue, lesion pictures typically show complex shapes; (4) the difference between the orientated object and the background is not discernible. This paper proposes a symmetry GAN detection network based on a one-stage detection network to tackle the challenges mentioned above. This paper employs the DeepLesion dataset, containing 10,594 CT scans (studies) of 4427 unique patients. The symmetry GANs proposed in this research consist of two distinct GAN models that serve different functions. A generative model is introduced ahead of the backbone to increase the input CT image series to address the typical problem of small sample size in medical datasets. Afterward, GAN models are added to the attention extraction module to generate attention masks. Furthermore, experimental data indicate that this strategy has significantly improved the model’s robustness. Eventually, the proposed method reaches 0.9720, 0.9858, and 0.9833 on P, R, and mAP, on the validation set. The experimental outcome shows that the suggested model outperforms other comparison models. In addition to this innovation, we are inspired by the innovation of the ResNet model in terms of network depth. Thus, we propose parallel multi-activation functions, an optimization method in the network width. It is theoretically proven that by adding coefficients to each base activation function and performing a softmax function on all coefficients, parallel multi-activation functions can express a single activation function, which is a unique ability compared to others. Ultimately, our model outperforms all comparison models in terms of P, R, and mAP, achieving 0.9737, 0.9845, and 0.9841. In addition, we encapsulate the model and build a related iOS application to make the model more applicable. The suggested model also won the second prize in the 2021 Chinese Collegiate Computing Competition.

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Cycle GAN-Based Data Augmentation For Multi-Organ Detection In CT Images Via Yolo

Cited by 42 publications

References 18 publications

NDFTC: A New Detection Framework of Tropical Cyclones from Meteorological Satellite Images with Deep Transfer Learning

NDFTC: A New Detection Framework of Tropical Cyclones from Meteorological Satellite Images with Deep Transfer Learning

Conditional TransGAN‐Based Data Augmentation for PCB Electronic Component Inspection

Symmetry GAN Detection Network: An Automatic One-Stage High-Accuracy Detection Network for Various Types of Lesions on CT Images

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