Automatic Depression Detection (ADD) methods utilize multimodal data, including text, audio, and visual information, to facilitate early clinical diagnosis and intervention. However, current ADD approaches predominantly model global or single-scale features, thus inadequately capturing fine-grained local depressive cues and insufficiently exploiting complementary information across modalities. In this paper, we propose MSTDD, a Multi-Scale Transformer-based method designed to address these limitations by effectively extracting and integrating depression-related features at multiple scales. Specifically, MSTDD employs modality-specific multi-scale encoders to capture hierarchical local depressive indicators, and introduces a multimodal cross-attention fusion mechanism to promote robust feature interaction between modalities. Extensive comparative evaluations and ablation experiments conducted on two benchmark depression datasets—DAIC-WOZ (AVEC 2017) and E-DAIC (AVEC 2019)—demonstrate that MSTDD outperforms state-of-the-art ADD methods, achieving average F1-scores of 0.82 on DAIC-WOZ and 0.80 on E-DAIC. Additionally, we conduct experiments comparing baseline models under different fusion strategies, further validating the effectiveness of our proposed method.