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

Abstract

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.