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Article summary:
1. This paper proposes a novel network called TransBTS for MRI Brain Tumor Segmentation, which uses the encoder-decoder structure and exploits Transformer in 3D CNN.
2. The encoder utilizes 3D CNN to extract volumetric spatial feature maps, while the decoder performs progressive upsampling to predict the detailed segmentation map.
3. Extensive experimental results on both BraTS 2019 and 2020 datasets show that TransBTS achieves comparable or higher results than previous state-of-the-art 3D methods for brain tumor segmentation on 3D MRI scans.
Article analysis:
The article is generally reliable and trustworthy as it provides evidence of extensive experiments conducted on both BraTS 2019 and 2020 datasets, showing that TransBTS achieves comparable or higher results than previous state-of-the-art 3D methods for brain tumor segmentation on 3D MRI scans. However, there are some potential biases that should be noted. For example, the article does not provide any information about possible risks associated with using this method or any counterarguments to its claims. Additionally, it does not present both sides of the argument equally and could be seen as promotional content due to its focus on the advantages of using this method over other existing methods. Furthermore, there is no mention of any missing points of consideration or unsupported claims made in the article.