  Over the past decade, Deep Convolutional Neural Networks have been widely
adopted for medical image segmentation and shown to achieve adequate
performance. However, due to the inherent inductive biases present in the
convolutional architectures, they lack understanding of long-range dependencies
in the image. Recently proposed Transformer-based architectures that leverage
self-attention mechanism encode long-range dependencies and learn
representations that are highly expressive. This motivates us to explore
Transformer-based solutions and study the feasibility of using
Transformer-based network architectures for medical image segmentation tasks.
Majority of existing Transformer-based network architectures proposed for
vision applications require large-scale datasets to train properly. However,
compared to the datasets for vision applications, for medical imaging the
number of data samples is relatively low, making it difficult to efficiently
train transformers for medical applications. To this end, we propose a Gated
Axial-Attention model which extends the existing architectures by introducing
an additional control mechanism in the self-attention module. Furthermore, to
train the model effectively on medical images, we propose a Local-Global
training strategy (LoGo) which further improves the performance. Specifically,
we operate on the whole image and patches to learn global and local features,
respectively. The proposed Medical Transformer (MedT) is evaluated on three
different medical image segmentation datasets and it is shown that it achieves
better performance than the convolutional and other related transformer-based
architectures. Code: https://github.com/jeya-maria-jose/Medical-Transformer
