本論文提出了一個以當前熱門主題Transformer及醫療影像常用的架構U-Net為基底的架構對多模態腦部腫瘤進行切割任務，並且在跳連結上加入了雙注意力機制以及在解碼器部分加入了深度監督以確保還原過程中的特徵最具有代表性並讓網路獲得更好的收斂效果。
本論文使用的資料集為BraTS2020，其中訓練集為369個樣本組成，驗證集 為125個樣本組成，對於驗證集官方並未提供相對應的切割標註，因此並不是作為模型挑選的資料集，而是需要上傳切割結果至CBICA IPP網站進行評分。
在前處理的部分，由於訓練樣本是由多台不同儀器取得，因此在像素值的
分布上為不同基準，故需要先對樣本進行Z-score Normalization將其標準化與切除樣本中極大極小值去除噪點。
在後處理的部分，由於訓練樣本中依據腫瘤嚴重程度分為HGG(High-grade Gliomas) 與 LGG(Low-grade gliomas)，而其在腫瘤表現上有些許不同，最明顯的部分為部分LGG是沒有增強腫瘤的，這將會造成評分時的極端現象，dice分數非0即0，因此後處理在這個資料集是必須的，其中包含將面積小於10像素的聯通元件(connected component)去除與將面積小於500 像素的增強腫瘤更正為壞死區域或非增強腫瘤。

Medical image segmentation is a relatively tricky task compared to natural image semantic segmentation. The dataset size for medical image were usually small caused by the difficulty of data retrieval. In this paper, a UNet-like architecture is proposed and implemented on MICCAI BraTS 2020 dataset, which contains 369 training data and 125 validation data. The model performance will be evaluated based on online scoring of validation dataset provided by CBICA IPP.
The proposed UNet-like architecture incorporated Transformer encoder to strengthen high-level feature extraction. The shortage of Transformer on low-level feature was made up by the UNet decoder skip connection, on which we designed an attention mechanism to separate spatial and channel attention. We also utilized deep supervision technique to deeply supervise the decoder learning process for better feature extraction.
The proposed UNet-like architecture achieved dice score of 0.78345, 0.90402, 0.83887 on ET, WT, TC respectively given by CBICA IPP online evaluation, which is a competitive result and surpassed most of the similar approaches in MICCAI BraTS2020 Challenge.

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