Summary of Mv-swin-t: Mammogram Classification with Multi-view Swin Transformer, by Sushmita Sarker et al.
MV-Swin-T: Mammogram Classification with Multi-view Swin Transformer
by Sushmita Sarker, Prithul Sarker, George Bebis, Alireza Tavakkoli
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed multi-view network leverages transformers to address challenges in mammographic image classification, introducing a novel shifted window-based dynamic attention block that effectively integrates and transfers information between views. This approach is tested on the CBIS-DDSM and Vin-Dr Mammo datasets, with transformer-based models showing improved performance under diverse settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors better detect breast cancer by using artificial intelligence to analyze mammography images from different angles at once. Current AI approaches only look at one view at a time, but radiologists use all views together to make diagnoses. The new approach uses something called transformers to combine information from all views and improve detection accuracy. |
Keywords
» Artificial intelligence » Attention » Image classification » Transformer
