YOLO-MED : Multi-Task Interaction Network for Biomedical Images

• URL: http://arxiv.org/abs/2403.00245v1
• Date: Fri, 1 Mar 2024 03:20:42 GMT
• Title: YOLO-MED : Multi-Task Interaction Network for Biomedical Images
• Authors: Suizhi Huang, Shalayiding Sirejiding, Yuxiang Lu, Yue Ding, Leheng Liu, Hui Zhou, Hongtao Lu
• Abstract summary: YOLO-Med is an efficient end-to-end multi-task network capable of concurrently performing object detection and semantic segmentation. Our model exhibits promising results in balancing accuracy and speed when evaluated on the Kvasir-seg dataset and a private biomedical image dataset.
• Score: 18.535117490442953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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