The content discusses the importance of object detection and semantic segmentation in biomedical image analysis. It introduces YOLO-Med, highlighting its backbone, neck, task-specific decoders, and cross-scale task-interaction module. The model aims to address limitations of existing multi-task networks by enhancing accuracy while maintaining real-time performance on datasets like Kvasir-seg and a private biomedical dataset.
Object detection is crucial for identifying abnormalities like polyps in colonoscopy videos or lesions in retinal fundus images. Semantic segmentation helps delineate object boundaries for quantitative assessment, widely used in segmenting polyps, tumor regions, and organs in CT scans. Deep learning models like YOLO series and RetinaNet have shown promise in biomedical image analysis.
Existing multi-task networks face challenges balancing accuracy and inference speed while neglecting cross-scale feature integration critical for biomedical image analysis. YOLO-Med addresses these issues with an end-to-end approach that combines object detection and semantic segmentation tasks efficiently.
The network employs a shared encoder with a backbone (CSPDarknet53) for feature extraction and a neck (FPN) for multi-scale feature fusion. Task-specific decoders handle segmentation and detection tasks separately, improving accuracy. A cross-scale task-interaction module facilitates information fusion between tasks at different scales.
Performance comparisons on Kvasir-seg dataset show YOLO-Med outperforming single-task networks like U-net or Polyp-PVT as well as other multi-task networks like UOLO or MULAN. The model achieves high accuracy while maintaining real-time speed across multiple metrics.
Ablation studies reveal the impact of modules within YOLO-Med, showing that the cross-scale task-interaction module significantly enhances segmentation performance. Combining this module with decoupled heads improves both detection accuracy and inference speed effectively.
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by Suizhi Huang... at arxiv.org 03-04-2024
https://arxiv.org/pdf/2403.00245.pdfDeeper Inquiries