YOLO-MED : Multi-Task Interaction Network for Biomedical Images
Suizhi Huang Shalayiding Sirejiding Yuxiang Lu Yue Ding Leheng Liu Hui Zhou Hongtao Lu
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.