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ECT-NAS: Searching Efficient CNN-Transformers Architecture for Medical Image Segmentation

The combination of convolution and Transformer applied to medical image segmentation has achieved great success. However, it still cannot reach extremely accurate segmentation on complex and low-contrast anatomical structures under lower calculation. To solve this problem, we propose ECT-NAS method to automatically search Efficient CNN-Transformers architecture for medical image segmentation, which featured with multi-scale search space. To better extract the global context in the search space of ECT-NAS, we carefully design a light transformer with local-global attention. Last, we proposed an efficient resource constrained search strategy that simultaneously optimizes the accuracy and cost (Params/FLOP) of the model. We evaluate ECT-NAS by conducting extensive experiments on synapse multi-organ, Chaos and ACDC datasets, showing that this approach achieves competitive performance over other segmentation methods, with fewer parameters and lower FLOPs.