# Towards Robust Natural-Looking Mammography Lesion Synthesis on Ipsilateral Dual-Views Breast Cancer Analysis

arXiv: https://arxiv.org/abs/2309.03506 · HTML: https://mlsanity.com/paper/2309.03506/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 7 Sep 2023.

## Builds on (most cited first, 20 of 20)

- [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)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Multi-view Convolutional Neural Networks for 3D Shape Recognition](https://mlsanity.com/paper/1505.00880/) (2015)
- [FDA: Fourier Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2004.05498/) (2020)
- [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018)
- [Volumetric and Multi-View CNNs for Object Classification on 3D Data](https://mlsanity.com/paper/1604.03265/) (2016)
- [Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models](https://mlsanity.com/paper/1909.11299/) (2019)
- [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)
- [3D Object Proposals using Stereo Imagery for Accurate Object Class Detection](https://mlsanity.com/paper/1608.07711/) (2016)
- [Deep Learning using Rectified Linear Units (ReLU)](https://mlsanity.com/paper/1803.08375/) (2018)
- [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)
- [High-Resolution Mammogram Synthesis using Progressive Generative Adversarial Networks](https://mlsanity.com/paper/1807.03401/) (2018)
- [In-context Cross-Density Adaptation on Noisy Mammogram Abnormalities Detection](https://mlsanity.com/paper/2306.06893/) (2023)

## Built on by (most cited first, 10 of 10)

- [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)
- [Adaptive Knowledge Transferring with Switching Dual-Student Framework for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2510.24366/) (2025)
- [Semi-Supervised Semantic Segmentation using Redesigned Self-Training for White Blood Cells](https://mlsanity.com/paper/2401.07278/) (2024)
- [Optimizing Breast Cancer Detection in Mammograms: A Comprehensive Study of Transfer Learning, Resolution Reduction, and Multi-View Classification](https://mlsanity.com/paper/2503.19945/) (2025)
- [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)
- [Label-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI](https://mlsanity.com/paper/2510.04705/) (2025)
- [Token-Based Dual-view Fusion and Adaptation of Large Vision Models for Breast Cancer Classification](https://mlsanity.com/paper/2607.06309/) (2026)

## Abstract (arXiv)

In recent years, many mammographic image analysis methods have been introduced for improving cancer classification tasks. Two major issues of mammogram classification tasks are leveraging multi-view mammographic information and class-imbalance handling. In the first problem, many multi-view methods have been released for concatenating features of two or more views for the training and inference stage. Having said that, most multi-view existing methods are not explainable in the meaning of feature fusion, and treat many views equally for diagnosing. Our work aims to propose a simple but novel method for enhancing examined view (main view) by leveraging low-level feature information from the auxiliary view (ipsilateral view) before learning the high-level feature that contains the cancerous features. For the second issue, we also propose a simple but novel malignant mammogram synthesis framework for upsampling minor class samples. Our easy-to-implement and no-training framework has eliminated the current limitation of the CutMix algorithm which is unreliable synthesized images with random pasted patches, hard-contour problems, and domain shift problems. Our results on VinDr-Mammo and CMMD datasets show the effectiveness of our two new frameworks for both multi-view training and synthesizing mammographic images, outperforming the previous conventional methods in our experimental settings.