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The spread of early-stage adenocarcinomas to locoregional lymph nodes is a critical event in disease progression of gastric cancer. Multiple instance learning (MIL) is widely employed in computational pathology to solve the absence of pixel-wise or patch-wise annotations in Whole Slide Image (WSI) datasets. MIL algorithms are typically applied a single-scale of WSIs, whereas pathologists usually aggregate diagnostic information across multiple scales. To this end, we propose a novel cross-scale MIL framework with multi-scale interactions to predict lymph node metastasis (LNM) from early-stage gastric cancer (EGC) WSIs. A novel cross-scale attention module is proposed to obtain cross-scale features from different resolutions with multi-scale interaction. Cross-Scale features, along with resolution-specific features, are then aggregated for the final slide-level prediction. Our experiments are conducted on a clinical cohort of WSIs from 740 patients with T1-stage gastric cancer. Our approach achieves a superior Area under the Curve (AUC) of 0.712, outperforming baseline MIL models. Additionally, multi-scale attention visualizations are generated to enhance the interpretability of automatic LNM diagnosis.Clinical Relevance- This study develops a deep learning model for LNM prediction from EGC WSIs, as opposed to lymph node specimens. Our research would be helpful to make treatment decisions for EGC patients, for example, avoid unnecessary lymph node resection.