While recent development in deep learning-based medical image segmentation has been fascinating, effectiveness mostly comes with the expense of expensive computing resources. In search of more affordable and convenient solutions, we propose a lightweight and faster yet effective medical image segmentation approach namely LightSeg. LightSeg leverages separable convolutional layers to decrease the model parameters and an attention mechanism to maintain segmentation quality. Our experimental evaluations on two different backbone networks (U-Net and ResU-Net) in segmenting the lungs from two publicly available chest X-ray datasets demonstrate the robustness of LightSeg while substantially reducing the network parameters.
|Title of host publication||ISBI 2022 - Proceedings|
|Subtitle of host publication||2022 IEEE International Symposium on Biomedical Imaging|
|State||Published - 2022|
|Event||19th IEEE International Symposium on Biomedical Imaging, ISBI 2022 - Kolkata, India|
Duration: Mar 28 2022 → Mar 31 2022
|Name||Proceedings - International Symposium on Biomedical Imaging|
|Conference||19th IEEE International Symposium on Biomedical Imaging, ISBI 2022|
|Period||3/28/22 → 3/31/22|
Bibliographical notePublisher Copyright:
© 2022 IEEE.
- Channel attention
- chest X-ray
ASJC Scopus subject areas
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging