A robust volumetric Transformer for accurate 3D tumor segmentation

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

53 Citations (Scopus)


We propose a Transformer architecture for volumetric segmentation, a challenging task that requires keeping a complex balance in encoding local and global spatial cues, and preserving information along all axes of the volume. Encoder of the proposed design benefits from self-attention mechanism to simultaneously encode local and global cues, while the decoder employs a parallel self and cross attention formulation to capture fine details for boundary refinement. Empirically, we show that the proposed design choices result in a computationally efficient model, with competitive and promising results on the Medical Segmentation Decathlon (MSD) brain tumor segmentation (BraTS) Task. We further show that the representations learned by our model are robust against data corruptions. Our code implementation is publicly available.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference Singapore, September 18–22, 2022 Proceedings, Part V
EditorsLinwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li
Place of PublicationCham Switzerland
Number of pages11
ISBN (Electronic)9783031164439
ISBN (Print)9783031164422
Publication statusPublished - 2022
EventMedical Image Computing and Computer-Assisted Intervention 2022 - Singapore, Singapore
Duration: 18 Sept 202222 Sept 2022
Conference number: 25th
https://link.springer.com/book/10.1007/978-3-031-16434-7 (Proceedings - Part 2)
https://conferences.miccai.org/2022/en/ (Website)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceMedical Image Computing and Computer-Assisted Intervention 2022
Abbreviated titleMICCAI 2022
Internet address


  • Pure volumetric transformer
  • Tumor segmentation

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