Summary of A Training Regime to Learn Unified Representations From Complementary Breast Imaging Modalities, by Umang Sharma et al.
A training regime to learn unified representations from complementary breast imaging modalities
by Umang Sharma, Jungkyu Park, Laura Heacock, Sumit Chopra, Krzysztof Geras
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 In this paper, researchers propose a machine learning approach to improve the accuracy of breast lesion detection using both Digital Breast Tomosynthesis (DBT) and Full Field Digital Mammograms (FFDM). The method leverages the complementary diagnostic signals from both modalities to learn high-level representations. This approach has been validated on a large-scale dataset, showing that it outperforms DBT- or FFDM-based models in detecting breast lesions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to find a small tumor in your breast. Doctors use special machines like Digital Breast Tomosynthesis (DBT) and Full Field Digital Mammograms (FFDM) to take pictures of your breasts. Both are good, but DBT is better at finding some kinds of tumors. However, it takes longer to get the results, which can be a problem. This paper suggests a new way to use both machines together to make tumor detection more accurate and faster. |
Keywords
» Artificial intelligence » Machine learning
