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Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images | SpringerLink
Source: link.springer.com
Article summary:
1. This article discusses the use of Swin Transformers for semantic segmentation of brain tumors in MRI images.
2. It references several studies that have used machine learning algorithms to improve brain tumor segmentation, radiomic features, and overall survival prediction.
3. The article also mentions the use of Bert pre-training of image transformers and 3D U-Net for volumetric segmentation from sparse annotation.
Article analysis:
The article is generally reliable and trustworthy as it provides a comprehensive overview of the current state of research on using Swin Transformers for semantic segmentation of brain tumors in MRI images. The article cites multiple studies that have used machine learning algorithms to improve brain tumor segmentation, radiomic features, and overall survival prediction, providing evidence to support its claims. Furthermore, it mentions the use of Bert pre-training of image transformers and 3D U-Net for volumetric segmentation from sparse annotation, which are both well-established methods in the field.
The only potential bias in the article is that it does not explore any counterarguments or alternative approaches to using Swin Transformers for semantic segmentation of brain tumors in MRI images. However, this is understandable given the scope and focus of the article.