MobileLookNet: A Lightweight Convolutional Neural Network for Detection of Osseous Metastasis Using Feature Fusion and Attention Strategies
Document Type
Conference Proceeding
Publication Date
4-26-2024
Abstract
This study introduces MobileLookNet, a novel lightweight architecture designed for detecting osseous metastasis in bone scintigrams on resource-constrained devices. By employing depthwise separable convolutions in parallel, utilizing inverted residuals, and integrating low-level and high-level features, MobileLookNet captures diverse levels of abstraction and extracts more individually expressive features. It outperforms traditional bone scintigraphy methods and state-of-the-art networks in metastasis detection while requiring significantly fewer floating-point operations (FLOPs) and parameters. Ablation studies reveal that feature fusion yields superior results compared to transformer-based attention strategies, highlighting the informative nature of low-level features in metastasis detection. Moreover, MobileLookNet demonstrates a trade-off between high accuracy, low FLOPs, and low parameters, where at most two can be achieved at a time. Overall, MobileLookNet shows promise in assisting nuclear medicine practitioners and enhancing metastasis detection in resource-constrained settings.
Recommended Citation
Irish Danielle Morales, Carlo Joseph Echon, Angelico Ruiz Teaño, Raphael Alampay, and Patricia Angela Abu. 2024. MobileLookNet: A Lightweight Convolutional Neural Network for Detection of Osseous Metastasis Using Feature Fusion and Attention Strategies. In Proceedings of the 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition (CVIPPR '24). Association for Computing Machinery, New York, NY, USA, Article 55, 1–6. https://doi.org/10.1145/3663976.3664235