# MV-Swin-T: Mammogram Classification with Multi-view Swin Transformer

arXiv: https://arxiv.org/abs/2402.16298 · HTML: https://mlsanity.com/paper/2402.16298/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 26 Feb 2024.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography](https://mlsanity.com/paper/2203.11205/) (2022)
- [Multi-view analysis of unregistered medical images using cross-view transformers](https://mlsanity.com/paper/2103.11390/) (2021)
- [Transformers Improve Breast Cancer Diagnosis from Unregistered Multi-View Mammograms](https://mlsanity.com/paper/2206.10096/) (2022)
- [ConnectedUNets++: Mass Segmentation from Whole Mammographic Images](https://mlsanity.com/paper/2210.13668/) (2022)

## Built on by (most cited first, 12 of 12)

- [XFMamba: Cross-Fusion Mamba for Multi-View Medical Image Classification](https://mlsanity.com/paper/2503.02619/) (2025)
- [Breast Cancer Detection from Multi-View Screening Mammograms with Visual Prompt Tuning](https://mlsanity.com/paper/2504.19900/) (2025)
- [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)
- [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)
- [Enhancing Screen Time Identification in Children with a Multi-View Vision Language Model and Screen Time Tracker](https://mlsanity.com/paper/2410.01966/) (2024)
- [Cross Feature Fusion of Fundus Image and Generated Lesion Map for Referable Diabetic Retinopathy Classification](https://mlsanity.com/paper/2411.03618/) (2024)
- [Hierarchical mutual distillation for multi-view fusion: Learning from all possible view combinations](https://mlsanity.com/paper/2411.10077/) (2024)
- [Integrating AI for Human-Centric Breast Cancer Diagnostics: A Multi-Scale and Multi-View Swin Transformer Framework](https://mlsanity.com/paper/2503.13309/) (2025)
- [Transformer-Based Explainable Deep Learning for Breast Cancer Detection in Mammography: The MammoFormer Framework](https://mlsanity.com/paper/2508.06137/) (2025)
- [Trusted Multi-View Deep Learning Classification of Fetal Congenital Heart Disease with Feature-level and Decision-level Fusion](https://mlsanity.com/paper/2606.15265/) (2026)
- [OTCHA: Optimal Transport-driven Confidence-aware Latent Hub Alignment for Multi-View Medical Image Classification](https://mlsanity.com/paper/2606.19838/) (2026)
- [Explicitly Aligned Multi-View Breast Cancer Risk Prediction from Variable-Length Screening Histories](https://mlsanity.com/paper/2610.16329/) (2026)

## Abstract (arXiv)

Traditional deep learning approaches for breast cancer classification has predominantly concentrated on single-view analysis. In clinical practice, however, radiologists concurrently examine all views within a mammography exam, leveraging the inherent correlations in these views to effectively detect tumors. Acknowledging the significance of multi-view analysis, some studies have introduced methods that independently process mammogram views, either through distinct convolutional branches or simple fusion strategies, inadvertently leading to a loss of crucial inter-view correlations. In this paper, we propose an innovative multi-view network exclusively based on transformers to address challenges in mammographic image classification. Our approach introduces a novel shifted window-based dynamic attention block, facilitating the effective integration of multi-view information and promoting the coherent transfer of this information between views at the spatial feature map level. Furthermore, we conduct a comprehensive comparative analysis of the performance and effectiveness of transformer-based models under diverse settings, employing the CBIS-DDSM and Vin-Dr Mammo datasets. Our code is publicly available at https://github.com/prithuls/MV-Swin-T