提出端到端多尺度病理图像分类模型，提升准确率并减少计算浪费。

Bag-based Multiple Instance Learning (MIL) approaches have emerged as the mainstream methodology for Whole Slide Image (WSI) classification. However, most existing methods adopt a segmented training strategy, which first extracts features using a pre-trained feature extractor and then aggregates these features through MIL. This segmented training approach leads to insufficient collaborative optimization between the feature extraction network and the MIL network, preventing end-to-end joint optimization and thereby limiting the overall performance of the model. Additionally, conventional methods typically extract features from all patches of fixed size, ignoring the multi-scale observation characteristics of pathologists. This not only results in significant computational resource waste when tumor regions represent a minimal proportion (as in the Camelyon16 dataset) but may also lead the model to suboptimal solutions. To address these limitations, this paper proposes an end-to-end multi-scale WSI classification framework that integrates multi-scale feature extraction with multiple instance learning. Specifically, our approach includes: (1) a semantic feature filtering module to reduce interference from non-lesion areas; (2) a multi-scale feature extraction module to capture pathological information at different levels; and (3) a multi-scale fusion MIL module for global modeling and feature integration. Through an end-to-end training strategy, we simultaneously optimize both the feature extractor and MIL network, ensuring maximum compatibility between them. Experiments were conducted on three cross-center datasets (DigestPath2019, BCNB, and UBC-OCEAN). Results demonstrate that our proposed method outperforms existing state-of-the-art approaches in terms of both accuracy (ACC) and AUC metrics.