ViTBIS: Vision Transformer for Biomedical Image Segmentation
This work addresses segmentation challenges in biomedical imaging for medical diagnosis, but it appears incremental as it builds on existing transformer and CNN architectures.
The paper tackles biomedical image segmentation by proposing ViTBIS, a network that combines multi-scale convolutions and transformer blocks, and reports that it outperforms previous state-of-the-art CNN and transformer models on multiple datasets using Dice score and Hausdorff distance metrics.
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 $1\times 1$, $3\times 3$ and $5\times 5$ 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.