Multi-organ lesion detection is of great clinical significance. However, lesions in different anatomical regions vary significantly in size and shape, and in CT images, lesion areas are typically small and similar to surrounding tissues, which increases the difficulty of detection. To address these challenges, this paper proposes an improved multi-organ small lesion detection algorithm based on the Salience-DETR model.Firstly, an Efficient Spatial-Channel Collaborative Attention (ESCA) mechanism is designed to reconstruct the multi-scale features extracted by the backbone, enhancing the model’s focus on important lesion information. Secondly, the DenseASPP and AugFusion modules are incorporated to optimize the cross-layer token fusion network, improving multi-scale feature fusion across different levels. Finally, an Inner-GIoU loss function is introduced to accelerate model convergence and improve the detection performance for small lesions.Experimental results show that, under the condition of 0.5 to 4 false positives per image, the improved model achieves average detection sensitivities of 83.26% and 82.33% on the public DeepLesion dataset and an external validation set, respectively. These results demonstrate that the proposed algorithm achieves high detection accuracy and good generalization performance for multi-organ small lesion detection, with promising potential for real-world clinical applications.