@inproceedings{
wu2024eenet,
title={E2{EN}et: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation},
author={Boqian Wu and Qiao Xiao and Shiwei Liu and Lu Yin and Mykola Pechenizkiy and Decebal Constantin Mocanu and Maurice van Keulen and Elena Mocanu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Xp8qhdmeb4}
}
Deep neural networks have evolved as the leading approach in 3D medical image segmentation due to their outstanding performance. However, the ever-increasing model size and computational cost of deep neural networks have become the primary barriers to deploying them on real-world, resource-limited hardware. To achieve both segmentation accuracy and efficiency, we propose a 3D medical image segmentation model called Efficient to Efficient Network (E2ENet), which incorporates two parametrically and computationally efficient designs. i. Dynamic sparse feature fusion (DSFF) mechanism: it adaptively learns to fuse informative multi-scale features while reducing redundancy. ii. Restricted depth-shift in 3D convolution: it leverages the 3D spatial information while keeping the model and computational complexity as 2D-based methods. We conduct extensive experiments on AMOS, Brain Tumor Segmentation and BTCV Challenge, demonstrating that E2ENet consistently achieves a superior trade-off between accuracy and efficiency than prior arts across various resource constraints. %In particular, with a single model and single scale, E2ENet achieves comparable accuracy on the large-scale challenge AMOS-CT, while saving over 69% parameter count and 27% FLOPs in the inference phase, compared with the previous
best-performing method. Our code has been made available at: https://github.com/boqian333/E2ENet-Medical.
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation
Boqian Wu Qiao Xiao Shiwei Liu Lu Yin Mykola Pechenizkiy Decebal Constantin Mocanu Maurice van Keulen Elena Mocanu