3D MRI brain tumor segmentation using autoencoder regularization
摘要： 問題： 3DMRI 腦腫瘤分割 方法： 描述了一個編碼器解碼器的結構，由於訓練數據集大小有限，添加變分自動編碼器分支以重建輸入圖像本身，以便使共享解碼器正規化並對解碼器層施加額外約束。 結果： 2018年挑戰第一名 介紹： 前面描述的是腦腫瘤等級，以及介紹數據集 貢獻： 1.在這項工作中，我們描述了我們用於多模式3D MRI的體積3D腦腫瘤分割的語義分割方法，該方法贏得了BraTS 2
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相關文章
- 1. Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks
- 2. Brain tumor segmentation with Deep Neural Networks
- 3. Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks_2017
- 4. Lung Cancer Tumor Region Segmentation Using Recurrent 3D-DenseUNet
- 5. Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks
- 6. Automatic Tumor Segmentation from MRI scans 閱讀筆記
- 7. Learning Contextual and Attentive Information for Brain Tumor Segmentation
- 8. 論文筆記：A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
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- 10. 論文翻譯-LSTM Multi-modal UNet for Brain Tumor Segmentation