BTA-DETR: a multi-domain cooperative perception model for brain tumor detection

To address the significant challenges in brain tumor magnetic resonance imaging detection-including high morphological heterogeneity of lesions, blurred boundaries, and severe background noise interference-this study proposes a multi-domain cooperative perception model, BTA-DETR (Brain Tumor Aware-DEtection TRansformer). First, we design a content-aware fusion unit, which leverages parallel spatial and channel branches alongside an adaptive gating mechanism to dynamically re-weight local textural details and global semantic features, thereby adaptively capturing the diverse morphologies of lesions. Second, we propose the learnable temperature attention module, which generates a pixel-level spatially heterogeneous temperature field via a lightweight convolutional branch to differentially modulate the sharpness of the attention distribution, thereby improving the model's localization performance in lesion regions with blurred boundaries. Finally, we construct a frequency-spatial cooperative module, which parses pathological textures through a tri-band gating mechanism in the frequency-domain branch, complemented by asymmetric depth-wise directional convolutions in the spatial-domain branch to supplement geometric boundary information, enhancing the representational capacity for fine-grained lesion features. Experimental results on the public Roboflow Brain Tumor Detection dataset demonstrate that, compared to the baseline RT-DETR, BTA-DETR improves mean average precision50 by 4.36 percentage points while reducing parameters by 0.82 M and computational cost by approximately 9.1%. Experimental results on the two external datasets, Kaggle BrainTumor and BraTS 2021 T1ce, further demonstrate that, under a dataset-specific retraining setting, BTA-DETR achieves higher detection metrics than the vanilla RT-DETR baseline.