In many recent years, multi-view mammogram analysis has been focused widely on AI-based cancer assessment. In this work, we aim to explore diverse fusion strategies (average and concatenate) and examine the model's learning behavior with varying individuals and fusion pathways, involving Coarse Layer and Fine Layer. The Ipsilateral Multi-View Network, comprising five fusion types (Pre, Early, Middle, Last, and Post Fusion) in ResNet-18, is employed. Notably, the Middle Fusion emerges as the most balanced and effective approach, enhancing deep-learning models' generalization performance by +2.06% (concatenate) and +5.29% (average) in VinDr-Mammo dataset and +2.03% (concatenate) and +3% (average) in CMMD dataset on macro F1-Score. The paper emphasizes the crucial role of layer assignment in multi-view network extraction with various strategies.

翻译：近年来，多视角乳房X线摄影分析在基于人工智能的癌症评估领域受到广泛关注。本研究旨在探索不同融合策略（平均与拼接），并考察模型在不同个体和融合路径（涉及粗层与细层）下的学习行为。我们采用包含五种融合类型（前融合、早期融合、中间融合、后期融合和后融合）的 ResNet-18 同侧多视角网络。值得注意的是，中间融合表现出最佳的平衡性与有效性，在 VinDr-Mammo 数据集上宏观 F1 分数提升 +2.06%（拼接）和 +5.29%（平均），在 CMMD 数据集上提升 +2.03%（拼接）和 +3%（平均），从而增强了深度学习模型的泛化性能。本文强调了在多视角网络提取中，不同策略下层级分配的关键作用。