  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.
