Summary of Mammo-clustering: a Multi-views Tri-level Information Fusion Context Clustering Framework For Localization and Classification in Mammography, by Shilong Yang et al.
Mammo-Clustering: A Multi-views Tri-level Information Fusion Context Clustering Framework for Localization and Classification in Mammography
by Shilong Yang, Chulong Zhang, Qi Zang, Juan Yu, Liang Zeng, Xiao Luo, Yexuan Xing, Xin Pan, Qi Li, Xiaokun Liang, Yaoqin Xie
First submitted to arxiv on: 23 Sep 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 Context Clustering Network with triple information fusion tackles the challenges of diagnosing breast cancer from mammography images. By leveraging context clustering methods, which are more computationally efficient and better at associating structural or pathological features, the approach is suitable for clinical tasks. The method integrates global information, feature-based local information, and patch-based local information through a triple information fusion mechanism. Evaluations on two public datasets, Vindr-Mammo and CBIS-DDSM, using five independent splits, demonstrate the effectiveness of the Context Clustering Network with an AUC of 0.828 on Vindr-Mammo and 0.805 on CBIS-DDSM, outperforming the next best method by 3.1% and 2.4%, respectively. The approach has strong potential as a scalable and cost-effective solution for large-scale mammography screening, enabling more efficient and accurate breast cancer detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to help doctors detect breast cancer from mammography images. It uses a special type of computer network that is good at finding tiny features in pictures. The approach combines information from different parts of the image to make better predictions. The researchers tested their method on two big datasets and found it works really well, beating other methods by a bit. This could be an important step forward in making breast cancer detection easier and more accurate. |
Keywords
» Artificial intelligence » Auc » Clustering
