Delving into Ipsilateral Mammogram Assessment under Multi-View Network

Delving into Ipsilateral Mammogram Assessment under Multi-View Network

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...

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Bibliographic Details
Main Authors: Truong, Thai Ngoc Toan, Nguyen, Thanh-Huy, Lam, Ba Thinh, Nguyen, Vu Minh Duy, Nguyen, Hong Phuc
Format: Journal Article
Language:English
Published: 05-09-2023
Subjects:
Computer Science - Computer Vision and Pattern Recognition
Online Access:Get full text
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Summary: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.
DOI:10.48550/arxiv.2309.02197