RTAF: Response aware Tensor Adaptive Function Block for Efficient 3D Medical Image Analysis

Volumetric medical features contain complex anatomical structures and simple repetitive responses, while dense 3D convolutions apply the same transformation to all feature responses. This uniform processing introduces unnecessary computation for simple responses and insufficient modeling capability for complex structures. Existing efficient operators mainly reduce redundancy or enhance feature interactions, but they lack a unified design that balances nonlinear representation and efficient local processing. We propose the Response Aware Tensor Adaptive Function (RTAF) block, a plug-and-play feature transformation module that dynamically combines nonlinear modeling and lightweight local processing according to feature characteristics. RTAF consists of three components: Router, Nonlinear Tensor Branch, and Local Convolution Branch. The Router generates an input-dependent modulation map that adjusts the inputs to the nonlinear and local processing branches.The Nonlinear Tensor Branch enables lightweight nonlinear feature modeling by combining Gaussian basis expansion with learnable centers and grouped CP-factorized tensor weights, while the Local Convolution Branch efficiently captures local spatial structures.Extensive experiments on six MedMNIST3D classification datasets and five Medical Segmentation Decathlon benchmarks demonstrate that RTAF achieves competitive performance across 3D ResNet-18/50 and nnU-Netv2, while introducing substantially lower parameter and computational costs than dense 3D convolution blocks.