YOLO-MED : Multi-Task Interaction Network for Biomedical Images
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911787103813632 |
|---|---|
| author | Huang, Suizhi Sirejiding, Shalayiding Lu, Yuxiang Ding, Yue Liu, Leheng Zhou, Hui Lu, Hongtao |
| author_facet | Huang, Suizhi Sirejiding, Shalayiding Lu, Yuxiang Ding, Yue Liu, Leheng Zhou, Hui Lu, Hongtao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00245 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | YOLO-MED : Multi-Task Interaction Network for Biomedical Images Huang, Suizhi Sirejiding, Shalayiding Lu, Yuxiang Ding, Yue Liu, Leheng Zhou, Hui Lu, Hongtao Computer Vision and Pattern Recognition 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. |
| title | YOLO-MED : Multi-Task Interaction Network for Biomedical Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.00245 |