Condensed UNETR: a lightweight medical image segmenter

Hybrid transformer-based segmentation methods have proven to be highly effective in analyzing medical images. However, they often demand significant computational resources for training and inference, which can be challenging for resource-scarce medical settings. In response, a novel framework named Condensed UNETR is introduced, which strikes a balance between precision and efficiency by combining the strengths of convolutional neural networks and transformers. The Condensed UNETR Block, a key element of this approach, facilitates efficient information flow through a self-attention mechanism decomposition and streamlined representation merging. The framework also incorporates the throughput metric as a measure of efficiency to monitor the model's resource usage. Experiments have shown that Condensed UNETR surpasses leading models in accuracy, size, and efficiency on devices with limited resources.