Medical portable devices are increasingly requiring high accuracy, speed, and low inference jitter to meet the urgent demands of healthcare. Modern hybrid attention-based segmentation frameworks enhance segmentation accuracy but add complexity that can slow operational speed, complicating practical deployment in resource-limited settings. We propose Slim UNETRV2, a simplified framework that utilizes only basic convolutional operations in both the encoder and decoder, thereby reducing execution time and inference jitter. The Slim UNETRV2 block, placed in skip connections at each hierarchical stage, aggregates extracted representations and improves global processing. Experiments demonstrate that Slim UNETRV2 outperforms state-of-the-art models in terms of accuracy, speed, and inference jitter for resource-constrained medical devices. Notably, Slim UNETRV2 achieves 93.89% dice accuracy and 2.90 mm HD95 on BraTS 2021, being 16.7 times faster with only 0.225 ms of inference jitter compared to SegMamba. Code: https://github.com/deepang-ai/Slim-UNETRV2.
Pang et al. (Wed,) studied this question.