A novel and simple approach to regularise attention frameworks and its efficacy in segmentation.

Deep neural networks with attention mechanism have shown promising results in many computer vision and medical image processing applications. Attention mechanisms help to capture long range interactions. Recently, more sophisticated attention mechanisms like criss-cross attention have been proposed for efficient computation of attention blocks. In this paper, we introduce a simple and low-overhead approach of adding noise to the attention block which we discover to be very effective when using an attention mechanism. Our proposed methodology of introducing regularisation in the attention block by adding noise makes the network more robust and resilient, especially in scenarios where there is limited training data. We incorporate this regularisation mechanism in the criss-cross attention block. This criss-cross attention block enhanced with regularisation is integrated in the bottleneck layer of a U-Net for the task of medical image segmentation. We evaluate our proposed framework on a challenging subset of the NIH dataset for segmenting lung lobes. Our proposed methodology results in improving dice-scores by 2.5 % in this context of medical image segmentation.