mlsanityData to 7 Oct 2026

ViTBIS: Vision Transformer for Biomedical Image Segmentation

arXiv 2201.05920 ↗First posted 15 Jan 2022eess.IV cs.CV cs.LGcited by 5

Not on the map. Why?

Abstract (arXiv)

In this paper, we propose a novel network named Vision Transformer for Biomedical Image Segmentation (ViTBIS). Our network splits the input feature maps into three parts with 1× 1, 3× 3 and 5× 5 convolutions in both encoder and decoder. Concat operator is used to merge the features before being fed to three consecutive transformer blocks with attention mechanism embedded inside it. Skip connections are used to connect encoder and decoder transformer blocks. Similarly, transformer blocks and multi scale architecture is used in decoder before being linearly projected to produce the output segmentation map. We test the performance of our network using Synapse multi-organ segmentation dataset, Automated cardiac diagnosis challenge dataset, Brain tumour MRI segmentation dataset and Spleen CT segmentation dataset. Without bells and whistles, our network outperforms most of the previous state of the art CNN and transformer based models using Dice score and the Hausdorff distance as the evaluation metrics.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 32 of its 44 references that are in the map. First posted on arXiv 15 Jan 2022.

Builds on

32 of 44 references.

References · 32

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20 more
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Built on by

Cited by year

Citing papers per year: 2022: 4; 2023: 1.

5 papers.

  1. 2022Vision Transformers in Medical Computer Vision -- A Contemplative Retrospectioncited by 22
  2. 2022A Comprehensive Survey of Transformers for Computer Visioncited by 13
  3. 2022Recent Progress in Transformer-based Medical Image Analysiscited by 9
  4. 2023Attention Mechanisms in Medical Image Segmentation: A Surveycited by 9
  5. 2022Transformers Meet Visual Learning Understanding: A Comprehensive Reviewcited by 5