# Delving into Ipsilateral Mammogram Assessment under Multi-View Network

arXiv: https://arxiv.org/abs/2309.02197 · HTML: https://mlsanity.com/paper/2309.02197/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 5 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening](https://mlsanity.com/paper/1903.08297/) (2019)
- [VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography](https://mlsanity.com/paper/2203.11205/) (2022)
- [High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks](https://mlsanity.com/paper/1703.07047/) (2017)
- [Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detection](https://mlsanity.com/paper/2105.10160/) (2021)
- [A novel multi-view deep learning approach for BI-RADS and density assessment of mammograms](https://mlsanity.com/paper/2112.04490/) (2021)
- [Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation](https://mlsanity.com/paper/2209.10478/) (2022)
- [In-context Cross-Density Adaptation on Noisy Mammogram Abnormalities Detection](https://mlsanity.com/paper/2306.06893/) (2023)

## Built on by (most cited first, 8 of 8)

- [Enhancing Vietnamese VQA through Curriculum Learning on Raw and Augmented Text Representations](https://mlsanity.com/paper/2503.03285/) (2025)
- [Emotic Masked Autoencoder with Attention Fusion for Facial Expression Recognition](https://mlsanity.com/paper/2403.13039/) (2024)
- [Blurry-Consistency Segmentation Framework with Selective Stacking on Differential Interference Contrast 3D Breast Cancer Spheroid](https://mlsanity.com/paper/2406.05349/) (2024)
- [A Hybrid CNN-VSSM model for Multi-View, Multi-Task Mammography Analysis: Robust Diagnosis with Attention-Based Fusion](https://mlsanity.com/paper/2507.16955/) (2025)
- [Semi-Supervised Semantic Segmentation using Redesigned Self-Training for White Blood Cells](https://mlsanity.com/paper/2401.07278/) (2024)
- [MamT4: Multi-view Attention Networks for Mammography Cancer Classification](https://mlsanity.com/paper/2411.01669/) (2024)
- [Modality-Specific Enhancement and Complementary Fusion for Semi-Supervised Multi-Modal Brain Tumor Segmentation](https://mlsanity.com/paper/2512.09801/) (2025)
- [Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound Diagnosis](https://mlsanity.com/paper/2401.07326/) (2024)

## Abstract (arXiv)

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.