YOLO-MED : Multi-Task Interaction Network for Biomedical Images
<|reference_start|>YOLO-MED : Multi-Task Interaction Network for Biomedical Images: Object detection and semantic segmentation are pivotal components in biomedical image analysis. Current single-task networks exhibit promising outcomes in both detection and segmentation tasks. Multi-task networks have gained prominence due to their capability to simultaneously tackle segmentation and detection tasks, while also accelerating the segmentation inference. Nevertheless, recent multi-task networks confront distinct limitations such as the difficulty in striking a balance between accuracy and inference speed. Additionally, they often overlook the integration of cross-scale features, which is especially important for biomedical image analysis. In this study, we propose an efficient end-to-end multi-task network capable of concurrently performing object detection and semantic segmentation called YOLO-Med. Our model employs a backbone and a neck for multi-scale feature extraction, complemented by the inclusion of two task-specific decoders. A cross-scale task-interaction module is employed in order to facilitate information fusion between various tasks. Our model exhibits promising results in balancing accuracy and speed when evaluated on the Kvasir-seg dataset and a private biomedical image dataset.<|reference_end|>
@article{huang2024yolo-med,
    title={YOLO-MED : Multi-Task Interaction Network for Biomedical Images},
    author={Suizhi Huang, Shalayiding Sirejiding, Yuxiang Lu, Yue Ding, Leheng
  Liu, Hui Zhou, Hongtao Lu},
    journal={arXiv preprint arXiv:2403.00245},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.00245},
    primaryClass={cs.CV}
}