@inproceedings{d89b9da220da4c7ebd36168f4ff9fdc4,
title = "E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation",
abstract = "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 computation cost of deep neural networks have become the primary barrier to deploying them on real-world resource-limited hardware. In pursuit of improving performance and efficiency, we propose a 3D medical image segmentation model, named Efficient to Efficient Network (E2ENet), incorporating two parametrically and computationally efficient designs: the Dynamic Sparse Feature Fusion (DSFF) mechanism, which adaptively learns to fuse informative multi-scale features while reducing redundancy, and Restricted depth-shift in 3D convolution, which leverages the 3D spatial information while keeping the model and computational complexity as 2D-based methods. We conduct extensive experiments on BTCV, AMOS-CT, and Brain Tumor Segmentation Challenge, demonstrating that E2ENet consistently achieves a superior trade-off between accuracy and efficiency than prior arts across various resource constraints. E2ENet achieves comparable accuracy on the large-scale challenge AMOS-CT, while saving over 68% parameter count and 29% 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.",
keywords = "Medical image segmentation, Sparse Training, Feature fusion",
author = "Boqian Wu and Qiao Xiao and Shiwei Liu and Lu Yin and Mykola Pechenizkiy and Mocanu, {Decebal Constantin} and {van Keulen}, Maurice and Elena Mocanu",
year = "2024",
language = "English",
booktitle = "38th Annual Conference on Neural Information Processing Systems, NeurIPS 2024",
publisher = "MLResearchPress",
note = "38th Annual Conference on Neural Information Processing Systems, NeurIPS 2024, NeurIPS 2024 ; Conference date: 09-12-2024 Through 15-12-2024",
url = "https://neurips.cc/Conferences/2024",
}