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Contents 1

Evaluating chest abnormalities detection : YOLOv7 and detection transformer with CycleGAN data augmentation / Yoshua Kaleb Purwanto ; Suk-Ho Lee ; Dae-Ki Kang 1

Abstract 1

1. Introduction 1

2. Related Work 2

2.1. You Only Look Once (YOLO) 2

2.2. Detection Transformer (DETR) 3

2.3. CycleGAN 4

2.4. Dataset : VinDr-CSR 5

3. Experiment Setup and Methodology 6

3.1. Setup Environment 6

3.2. Data Augmentation Scenario 6

3.3. Chest Abnormalities Detection 7

4. Result and Discussion 7

5. Conclusion 9

References 9

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Patient-specific mapping between myocardium and coronary arteries using myocardial thickness variation Dongjin Han p. 187-194

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Evaluating chest abnormalities detection : YOLOv7 and detection transformer with CycleGAN data augmentation Yoshua Kaleb Purwanto, Suk-Ho Lee, Dae-Ki Kang p. 195-204

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참고문헌 (15건) : 자료제공( 네이버학술정보 )

참고문헌 목록에 대한 테이블로 번호, 참고문헌, 국회도서관 소장유무로 구성되어 있습니다.
번호 참고문헌 국회도서관 소장유무
1 A. Bochkovskiy, C. Wang, H. M. Liao, and R. Girshick, "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors," Apr. 2021. DOI: https://doi.org/10.48550/arXiv.2207.02696 미소장
2 J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. DOI: https://doi.org/10.48550/arXiv.1506.02640 미소장
3 J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “YOLOv3: An Incremental Improvement,” Apr. 2018. DOI: https://doi.org/10.48550/arXiv.1804.02767 미소장
4 A. Bochkovskiy, C. Wang, and H. M. Liao, "YOLOv4: Optimal Speed and Accuracy of Object Detection," Apr. 2020. DOI: https://doi.org/10.48550/arXiv.2004.10934 미소장
5 N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, "End-to-end object detection with transformers," in European Conference on Computer Vision (ECCV), 2020, pp. 213–229. Springer. DOI: https://doi.org/10.48550/arXiv.2005.12872 미소장
6 A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Kaiser, and I. Polosukhin, "Attention is all you need," in Advances in neural information processing systems (NeurIPS), 2017. DOI: https://doi.org/10.48550/arXiv.1706.03762 미소장
7 A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al., "An image is worth 16x16 words: Transformers for image recognition at scale," Oct. 2020. DOI: https://doi.org/10.48550/arXiv.2010.11929 미소장
8 Z. Chen, Y. Duan, W. Wang, J. He, T. Lu, J. Dai, and Y. Qiao, "Vision Tranformer Adapter for Dense Predictions," In Proceedings of the 9th International Conference on Learning Representations (ICLR), Feb. 2023. DOI: https://doi.org/10.48550/arXiv.2205.08534 미소장
9 H. Q. Nguyen et al., "VinDr-CXR: An Open Dataset of Chest X-rays with Radiologist’s Annotations," Jan. 2022. DOI: https://doi.org/10.48550/arXiv.2012.15029 미소장
10 J. Zhu, T. Park, P. Isola, and A. A. Efros, "Unpaired image-to-image translation using cycle-consistent adversarial networks," in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017. DOI: https://doi.org/10.48550/arXiv.1703.10593 미소장
11 R. Zhang, P. Isola, and A. A. Efros, "Colorful image colorization," in European Conference on Computer Vision (ECCV), 2016. DOI: https://doi.org/10.48550/arXiv.1603.08511 미소장
12 L. A. Gatys, A. S. Ecker, and M. Bethge, "Image style transfer using convolutional neural networks," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 미소장
13 M. Tan and Q. V. Le, "EfficientDet: Scalable and efficient object detection," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. DOI: https://doi.org/10.48550/arXiv.1911.09070 미소장
14 J. Hosang, R. Benenson, P. Dollár, and B. Schiele, "Learning non-maximum suppression," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. DOI: https://doi.org/10.48550/arXiv.1705.02950 미소장
15 D. Bahdanau, K. Cho, and Y. Bengio, "Neural machine translation by jointly learning to align and translate," in Proceedings of the 3rd International Conference on Learning Representations (ICLR), Sep. 2014. https://doi.org/10.48550/arXiv.1409.0473 미소장

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