TransReg: Cross-transformer as auto-registration module for multi-view mammogram mass detection
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Abstract (arXiv)
Screening mammography is the most widely used method for early breast cancer detection, significantly reducing mortality rates. The integration of information from multi-view mammograms enhances radiologists' confidence and diminishes false-positive rates since they can examine on dual-view of the same breast to cross-reference the existence and location of the lesion. Inspired by this, we present TransReg, a Computer-Aided Detection (CAD) system designed to exploit the relationship between craniocaudal (CC), and mediolateral oblique (MLO) views. The system includes cross-transformer to model the relationship between the region of interest (RoIs) extracted by siamese Faster RCNN network for mass detection problems. Our work is the first time cross-transformer has been integrated into an object detection framework to model the relation between ipsilateral views. Our experimental evaluation on DDSM and VinDr-Mammo datasets shows that our TransReg, equipped with SwinT as a feature extractor achieves state-of-the-art performance. Specifically, at the false positive rate per image at 0.5, TransReg using SwinT gets a recall at 83.3% for DDSM dataset and 79.7% for VinDr-Mammo dataset. Furthermore, we conduct a comprehensive analysis to demonstrate that cross-transformer can function as an auto-registration module, aligning the masses in dual-view and utilizing this information to inform final predictions. It is a replication diagnostic workflow of expert radiologists
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 45 references that are in the map. First posted on arXiv 9 Nov 2023.
Builds on
22 of 45 references.
References · 22
- 2017Attention Is All You Needcited by 59,932
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2020End-to-End Object Detection with Transformerscited by 7,384
- 2017Mask R-CNNcited by 7,259
- 2016Feature Pyramid Networks for Object Detectioncited by 5,021
- 2018YOLOv3: An Incremental Improvementcited by 2,652
- 2019Distance-IoU Loss: Faster and Better Learning for Bounding Box Regressioncited by 454
- 2021Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformerscited by 316
- 2017Relation Networks for Object Detectioncited by 305
10 more
- 2019Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screeningcited by 94
- 2022VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammographycited by 83
- 2017Detecting and classifying lesions in mammograms with Deep Learningcited by 47
- 2021Multi-view analysis of unregistered medical images using cross-view transformerscited by 35
- 2017High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networkscited by 25
- 2021Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detectioncited by 22
- 2016Breast Mass Classification from Mammograms using Deep Convolutional Neural Networkscited by 12
- 2019Cross-view Relation Networks for Mammogram Mass Detectioncited by 11
- 2019Improving localization-based approaches for breast cancer screening exam classificationcited by 2
- 2021Breast Mass Detection with Faster R-CNN: On the Feasibility of Learning from Noisy Annotationscited by 1
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Citing papers per year: 2026: 2.
2 papers.