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arXiv · 2309.02197

Delving into Ipsilateral Mammogram Assessment under Multi-View Network

Thai Ngoc Toan Truong·Thanh-Huy Nguyen·Ba Thinh Lam·Vu Minh Duy Nguyen·Hong Phuc Nguyen
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Abstract

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.

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Thai Ngoc Toan Truong, Thanh-Huy Nguyen, Ba Thinh Lam, Vu Minh Duy Nguyen, Hong Phuc Nguyen. 2023-09-05. Delving into Ipsilateral Mammogram Assessment under Multi-View Network. https://arxiv.org/abs/2309.02197

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