![CNNを旋喘した猟[Pereira et al., 2016]
S└rgio Pereira, Adriano Pinto, Victor Alves, and Carlos A Silva. Brain tumor segmentation using convolutional neural networks in mri images. IEEE transactions on
medical imaging, 35(5):1240C1251, 2016.](https://image.slidesharecdn.com/unsuperviseddetectionoflesionsinbrainmriusingconstrainedadversarialauto-encoders-181105120547/85/Unsupervised-detection-of-lesions-in-brain-mri-using-constrained-adversarial-auto-encoders-8-320.jpg)
![CNNを旋喘した猟[Pereira et al., 2016]
S└rgio Pereira, Adriano Pinto, Victor Alves, and Carlos A Silva. Brain tumor segmentation using convolutional neural networks in mri images. IEEE transactions on
medical imaging, 35(5):1240C1251, 2016.](https://image.slidesharecdn.com/unsuperviseddetectionoflesionsinbrainmriusingconstrainedadversarialauto-encoders-181105120547/85/Unsupervised-detection-of-lesions-in-brain-mri-using-constrained-adversarial-auto-encoders-9-320.jpg)
![CNNを旋喘した猟[Kamnitsas et al., 2017]
Konstantinos Kamnitsas, Christian Ledig, Virginia FJ Newcombe, Joanna P Simpson, Andrew D Kane, David K Menon, Daniel Rueckert, and Ben Glocker. Efficient
multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Medical image analysis, 36:61C78, 2017.](https://image.slidesharecdn.com/unsuperviseddetectionoflesionsinbrainmriusingconstrainedadversarialauto-encoders-181105120547/85/Unsupervised-detection-of-lesions-in-brain-mri-using-constrained-adversarial-auto-encoders-10-320.jpg)
![CNNを旋喘した猟[Kamnitsas et al., 2017]
Konstantinos Kamnitsas, Christian Ledig, Virginia FJ Newcombe, Joanna P Simpson, Andrew D Kane, David K Menon, Daniel Rueckert, and Ben Glocker. Efficient
multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Medical image analysis, 36:61C78, 2017.](https://image.slidesharecdn.com/unsuperviseddetectionoflesionsinbrainmriusingconstrainedadversarialauto-encoders-181105120547/85/Unsupervised-detection-of-lesions-in-brain-mri-using-constrained-adversarial-auto-encoders-11-320.jpg)
Unsupervised detection of lesions in brain mri using constrained adversarial auto encoders
- 1. Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders Masataka Nishimori 2018-11-05
- 2. B初猟 - Title: Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders - Authors: Xiaoran Chen, Ender Konukoglu - Submitted on: 13 Jun 2018
- 4. はじめに(冩梢の朕議/嘘尚) - 宗械宀のMRIやCTデ`タは掲械に楚が謹いが押笋里△襯禰`タは楚が富ない - さらにM佩業栽いまで秘れると畠何で方箭しかない栽もある - 宗械宀のデ`タの方 >> 鵑琳∪の方 - また楚があったとしても屎盾デ`タとして旋喘辛嬬なデ`タになっていない栽が謹い
- 7. 噫: CNNで押箜薐 - BRATSやISLESといった[のI囃渇竃デ`タセットが贋壓 - CNNを旋喘して措い來嬬を竃す冩梢はすでに贋壓 - しかしながらデ`タセットを恬るコストが掲械に互い
- 8. CNNを旋喘した猟[Pereira et al., 2016] S└rgio Pereira, Adriano Pinto, Victor Alves, and Carlos A Silva. Brain tumor segmentation using convolutional neural networks in mri images. IEEE transactions on medical imaging, 35(5):1240C1251, 2016.
- 9. CNNを旋喘した猟[Pereira et al., 2016] S└rgio Pereira, Adriano Pinto, Victor Alves, and Carlos A Silva. Brain tumor segmentation using convolutional neural networks in mri images. IEEE transactions on medical imaging, 35(5):1240C1251, 2016.
- 10. CNNを旋喘した猟[Kamnitsas et al., 2017] Konstantinos Kamnitsas, Christian Ledig, Virginia FJ Newcombe, Joanna P Simpson, Andrew D Kane, David K Menon, Daniel Rueckert, and Ben Glocker. Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Medical image analysis, 36:61C78, 2017.
- 11. CNNを旋喘した猟[Kamnitsas et al., 2017] Konstantinos Kamnitsas, Christian Ledig, Virginia FJ Newcombe, Joanna P Simpson, Andrew D Kane, David K Menon, Daniel Rueckert, and Ben Glocker. Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Medical image analysis, 36:61C78, 2017.
- 12. 枠佩冩梢
- 13. AnoGAN - 謹蛍恷兜のGANによる械返返隈 ## 鮄地 - 匳喘鮫颪砲ける鴫纂のk - マルウェア返r狼双デ`タは1肝圷の鮫颯禰`タと深える
- 15. AnoGAN - GANで伏撹した鮫颪Accuracy Scoreから械業(Accuracy 皆界看姻艶)を麻竃
- 16. 戻宛返隈
- 17. 戻宛返隈 1. 宗械宀の光スライス鮫颪VAEやAAEで僥ばせる 2. 採かしら械のある屏箭をそのモデルに秘薦として鮫颪鰓撹する 3. 壅撹した鮫颪挽鮫颪箸硫邨屬鯣，襪海箸韮械なI囃を渇竃する VAE AAE 圷鮫 壅撹鮫
- 18. 戻宛返隈 1. 宗械宀の光スライス鮫颪VAEやAAEで僥ばせる 2. 採かしら械のある屏箭をそのモデルに秘薦として鮫颪鰓撹する 3. 壅撹した鮫颪挽鮫颪箸硫邨屬鯣，襪海箸韮械なI囃を渇竃する VAE AAE 圷鮫 壅撹鮫
- 19. 戻宛返隈 1. 宗械宀の光スライス鮫颪VAEやAAEで僥ばせる 2. 採かしら械のある屏箭をそのモデルに秘薦として鮫颪鰓撹する 3. 壅撹した鮫颪挽鮫颪箸硫邨屬僚~､鯣，襪海箸韮械なI囃を渇竃する 圷鮫 壅撹鮫 鮫餡邨屬僚~
- 20. gY圭隈
- 21. gYで聞うモデル - VAEとAAEを旋喘 - AAE: VAEのJSダイバ`ジェンス井 - VAEよりAAEのほうが捻壓腎gのW_楕が互いらしい - GANの旗わりにWasserStein GANを旋喘 - GPを旋喘 - VAEとAAE(λ=0, 0.5, 1.0)の4パタ`ンでgY - λはp払v方の屎t晒喘のパラメ`タ
- 22. gY潤惚
- 23. /テストデ`タ - デ`タ - HCP(Human Connectome Project)のT2{鮫 - 35屏箭 - テストデ`タ - BRATS(the Multimodal Brain Tumor Segmentation) Challenge 2015 - 42屏箭
- 24. gY潤惚 - 宗械宀の鮫駸撹 2砂“な鮫颪淋撹 双aは、HCPデ`タセットからの瓜Y宀からのT2{鮫 双b?eは、VAE、AAE、λ = 0.5の AAE、λ = 1.0の AAEによる壅撹
- 26. 屎械と呟械での蛍下の餓
- 27. 來嬬 - ピクセルg了でのROC爆 - 捻壓腎gの嶷みを嶷勣したAAE(λ=1)が匯桑來嬬措かった - これ(λ=5)とかやったらどうなんでしょ
- 29. 恷除の強
- 30. Mondal et al., 2018 - Title: Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning - Authors: Arnab Kumar Mondal, Jose Dolz and Christian Desrosiers - Submitted on 29 Oct 2018
- 31. Mondal et al., 2018 - 方屏箭のラベルしかない彜BでもGANで屎盾パッチを寄楚恬撹
- 32. まとめ - VAEやAAEを旋喘した械返(押奮)返隈をB初 - 宗械宀の屏箭をVAEに僥ばせることで^何MRから[の奮に撹孔 - 採かしら鴫纂を宇えている栽は鮫駸撹rの鮫颪挽鮫颪鵬遒でることがわかった - GANによるラベルデ`タの恬撹など書瘁も冩梢されそうな蛍勸
- 33. References Papers - Sun, Liyan, et al. "An Adversarial Learning Approach to Medical Image Synthesis for Lesion Removal." arXiv preprint arXiv:1810.10850 (2018). - Schlegl, Thomas, et al. "Unsupervised anomaly detection with generative adversarial networks to guide marker discovery." International Conference on Information Processing in Medical Imaging. Springer, Cham, 2017. - AnoGANの圷猟 - Frid-Adar, Maayan, et al. "GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification." arXiv preprint arXiv:1803.01229 (2018). - 弦何CTからGANで押奮 - Tang, Youbao, et al. "CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement." International Workshop on Machine Learning in Medical Imaging. Springer, Cham, 2018. - Ben-Cohen, Avi, et al. "Cross-Modality Synthesis from CT to PET using FCN and GAN Networks for Improved Automated Lesion Detection." arXiv preprint arXiv:1802.07846 (2018).
- 34. References Datasets - BRATS: Multimodal Brain Tumor Segmentation Challenge 2018 - ISLES: Ischemic Stroke Lesion Segmentation - HCP: Human Connectome Project
- 35. References Blogs - habakan6.‐猟iみ／GANを旋喘した械返まとめ. Hatena Blog. - https://openreview.net/forum?id=H1nGLZ2oG, OpenReview.net. - 釦i宀の吭がdっていて歌深になる - GANによる械返返隈 anoGAN のB初 - https://www.renom.jp/ja/notebooks/tutorial/generative-model/anoGAN/notebook.html