nnFormer: Volumetric Medical Image Segmentation via a 3D Transformer

Transformer, the model of choice for natural language processing, has drawn scant attention from the medical imaging community. Additionally, we show that nnFormer and nnUNet are highly complementary to each other in model ensembling.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 Given the ability to exploit long-term dependencies, transformers are promising to help atypical convolutional neural networks to learn more contextualized visual representations.
• 2 However, most of recently proposed transformer-based segmentation approaches simply treated transformers as assisted modules to help encode global context into convolutional representations.
• 3 To address this issue, we introduce nnFormer (i.e., not-another transFormer), a 3D transformer for volumetric medical image segmentation.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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