Rolling convolution filters for lightweight neural networks in medical image analysis

To introduce a filter design element called rolling convolution filters for developing lightweight convolutional neural networks (CNNs) in medical image analysis, aiming to reduce model complexity and memory footprint without compromising performance.

Rolling convolution filters were generated by performing a channel-wise rolling operation on a single base filter, creating unique filters while restricting the learnable parameters. The method was applied to various two- and three-dimensional medical image analysis tasks, including reconstruction, segmentation, and classification across MRI, CT, and OCT modalities. The performance was compared with that of standard CNNs and other lightweight architectures.

The proposed rolling convolution filters substantially reduced the number of parameters and model size compared with standard CNNs, with a negligible increase in performance error. For quantitative susceptibility mapping, the rolling filter approach achieved results comparable to those of state-of-the-art methods with 6× fewer parameters. In COVID-19 anomaly segmentation, rolling filters performed on par with existing lightweight models while having $\sim 68\times$ fewer parameters. For OCT classification, rolling filters maintained accuracy while significantly reducing the model size (49×).

Rolling convolution filters offer an effective approach for designing lightweight CNNs for medical image analysis tasks, providing substantial reductions in model complexity and memory requirements while maintaining a performance comparable to that of larger models. This method can be easily incorporated into existing architectures and shows promise for deploying efficient deep learning models in resource-constrained medical imaging settings.