# Multi-view analysis of unregistered medical images using cross-view transformers

arXiv: https://arxiv.org/abs/2103.11390 · HTML: https://mlsanity.com/paper/2103.11390/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 21 Mar 2021.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [Visual Transformers: Token-based Image Representation and Processing for Computer Vision](https://mlsanity.com/paper/2006.03677/) (2020)
- [Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening](https://mlsanity.com/paper/1903.08297/) (2019)
- [Large Scale Automated Reading of Frontal and Lateral Chest X-Rays using Dual Convolutional Neural Networks](https://mlsanity.com/paper/1804.07839/) (2018)
- [Quantifying the Value of Lateral Views in Deep Learning for Chest X-rays](https://mlsanity.com/paper/2002.02582/) (2020)
- [Cross-view Relation Networks for Mammogram Mass Detection](https://mlsanity.com/paper/1907.00528/) (2019)

## Built on by (most cited first, 30 of 36)

- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [A Survey of Visual Transformers](https://mlsanity.com/paper/2111.06091/) (2021)
- [A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization](https://mlsanity.com/paper/2201.09206/) (2022)
- [Transformers in Medical Image Analysis: A Review](https://mlsanity.com/paper/2202.12165/) (2022)
- [Surface Vision Transformers: Attention-Based Modelling applied to Cortical Analysis](https://mlsanity.com/paper/2203.16414/) (2022)
- [Towards Evaluating Explanations of Vision Transformers for Medical Imaging](https://mlsanity.com/paper/2304.06133/) (2023)
- [Transformers in Healthcare: A Survey](https://mlsanity.com/paper/2307.00067/) (2023)
- [MV-Swin-T: Mammogram Classification with Multi-view Swin Transformer](https://mlsanity.com/paper/2402.16298/) (2024)
- [Transformers Improve Breast Cancer Diagnosis from Unregistered Multi-View Mammograms](https://mlsanity.com/paper/2206.10096/) (2022)
- [Cross-Field Transformer for Diabetic Retinopathy Grading on Two-field Fundus Images](https://mlsanity.com/paper/2211.14552/) (2022)
- [Mammo-CLIP: Leveraging Contrastive Language-Image Pre-training (CLIP) for Enhanced Breast Cancer Diagnosis with Multi-view Mammography](https://mlsanity.com/paper/2404.15946/) (2024)
- [Cardiovascular Disease Detection from Multi-View Chest X-rays with BI-Mamba](https://mlsanity.com/paper/2405.18533/) (2024)
- [Multi-View Hypercomplex Learning for Breast Cancer Screening](https://mlsanity.com/paper/2204.05798/) (2022)
- [Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning](https://mlsanity.com/paper/2306.00003/) (2023)
- [MoViT: Memorizing Vision Transformers for Medical Image Analysis](https://mlsanity.com/paper/2303.15553/) (2023)
- [A Deep Network for Explainable Prediction of Non-Imaging Phenotypes using Anatomical Multi-View Data](https://mlsanity.com/paper/2401.04579/) (2024)
- [XFMamba: Cross-Fusion Mamba for Multi-View Medical Image Classification](https://mlsanity.com/paper/2503.02619/) (2025)
- [MultiNet with Transformers: A Model for Cancer Diagnosis Using Images](https://mlsanity.com/paper/2301.09007/) (2023)
- [DisAsymNet: Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms using Self-adversarial Learning](https://mlsanity.com/paper/2307.02935/) (2023)
- [M&M: Tackling False Positives in Mammography with a Multi-view and Multi-instance Learning Sparse Detector](https://mlsanity.com/paper/2308.06420/) (2023)
- [TransReg: Cross-transformer as auto-registration module for multi-view mammogram mass detection](https://mlsanity.com/paper/2311.05192/) (2023)
- [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)
- [Deep is a Luxury We Don't Have](https://mlsanity.com/paper/2208.06066/) (2022)
- [End-To-End Prediction of Knee Osteoarthritis Progression With Multi-Modal Transformers](https://mlsanity.com/paper/2307.00873/) (2023)
- [Case-level Breast Cancer Prediction for Real Hospital Settings](https://mlsanity.com/paper/2310.12677/) (2023)
- [HawkI: Homography & Mutual Information Guidance for 3D-free Single Image to Aerial View](https://mlsanity.com/paper/2311.15478/) (2023)
- [MammoClean: Toward Reproducible and Bias-Aware AI in Mammography through Dataset Harmonization](https://mlsanity.com/paper/2511.02400/) (2025)
- [Random Token Fusion for Multi-View Medical Diagnosis](https://mlsanity.com/paper/2410.15847/) (2024)
- [Hierarchical mutual distillation for multi-view fusion: Learning from all possible view combinations](https://mlsanity.com/paper/2411.10077/) (2024)

## Abstract (arXiv)

Multi-view medical image analysis often depends on the combination of information from multiple views. However, differences in perspective or other forms of misalignment can make it difficult to combine views effectively, as registration is not always possible. Without registration, views can only be combined at a global feature level, by joining feature vectors after global pooling. We present a novel cross-view transformer method to transfer information between unregistered views at the level of spatial feature maps. We demonstrate this method on multi-view mammography and chest X-ray datasets. On both datasets, we find that a cross-view transformer that links spatial feature maps can outperform a baseline model that joins feature vectors after global pooling.