The Fully Convolutional Transformer for Medical Image Segmentation
This work addresses the problem of accurate and efficient medical image segmentation for healthcare applications, presenting a novel hybrid model that is incremental but offers specific gains.
The paper tackles the challenge of adapting transformers for fine-grained medical image segmentation by proposing the Fully Convolutional Transformer (FCT), which combines CNNs and transformers to capture long-range dependencies and hierarchical features, resulting in outperforming existing transformer architectures by margins of 1.1% to 4.4% on dice metrics across multiple datasets with up to five times fewer parameters.
We propose a novel transformer model, capable of segmenting medical images of varying modalities. Challenges posed by the fine grained nature of medical image analysis mean that the adaptation of the transformer for their analysis is still at nascent stages. The overwhelming success of the UNet lay in its ability to appreciate the fine-grained nature of the segmentation task, an ability which existing transformer based models do not currently posses. To address this shortcoming, we propose The Fully Convolutional Transformer (FCT), which builds on the proven ability of Convolutional Neural Networks to learn effective image representations, and combines them with the ability of Transformers to effectively capture long-term dependencies in its inputs. The FCT is the first fully convolutional Transformer model in medical imaging literature. It processes its input in two stages, where first, it learns to extract long range semantic dependencies from the input image, and then learns to capture hierarchical global attributes from the features. FCT is compact, accurate and robust. Our results show that it outperforms all existing transformer architectures by large margins across multiple medical image segmentation datasets of varying data modalities without the need for any pre-training. FCT outperforms its immediate competitor on the ACDC dataset by 1.3%, on the Synapse dataset by 4.4%, on the Spleen dataset by 1.2% and on ISIC 2017 dataset by 1.1% on the dice metric, with up to five times fewer parameters. Our code, environments and models will be available via GitHub.