Precision in Pathology: PMA-DETR Elevates Tumor Lesion Detection

Abstract

The DETR series, known for its end-to-end object detection models, has gained significant attention for its performance. RT-DETR excels with higher accuracy and faster real-time inference. However, applying these models to medical imaging poses challenges, such as low-contrast and complex lesion structures, which can reduce effectiveness. When detecting tumors, models may overfit due to the distinct differences and variability between different cases, affecting generalization and accuracy. To address these challenges, we propose a multi-view parallel feature extraction module, specifically for tumor detection. This module includes adaptive preprocessing, joint axial and channel attention, multi-pooling angular attention to enhance relevant features and reduce redundancy. Additionally, axial dynamic deformable convolution is used to improve adaptability and robustness. The resulting PMA-DETR architecture achieves state-of-the-art tumor detection while maintaining real-time processing.