Summary of Towards Non-invasive and Personalized Management Of Breast Cancer Patients From Multiparametric Mri Via a Large Mixture-of-modality-experts Model, by Luyang Luo et al.
Towards Non-invasive and Personalized Management of Breast Cancer Patients from Multiparametric MRI via A Large Mixture-of-Modality-Experts Model
by Luyang Luo, Mingxiang Wu, Mei Li, Yi Xin, Qiong Wang, Varut Vardhanabhuti, Winnie CW Chu, Zhenhui Li, Juan Zhou, Pranav Rajpurkar, Hao Chen
First submitted to arxiv on: 8 Aug 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 paper presents a large mixture-of-modality-experts (MOME) model that integrates multiple sequences of breast magnetic resonance imaging (MRI) information to develop a non-invasive method for personalized breast cancer management. The MOME model is trained and evaluated on a curated dataset of 5,205 patients from three hospitals in China. The model demonstrates accurate and robust identification of breast cancer, achieving comparable performance to senior radiologists and outperforming junior radiologists. The model also shows promise in reducing the need for biopsies, classifying triple-negative breast cancer, and predicting pathological complete response to neoadjuvant chemotherapy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The MOME model is a new way to use artificial intelligence to help doctors diagnose and manage breast cancer. It uses information from different types of MRI scans to make a diagnosis. The model was tested on a large group of patients and showed that it can be just as good as experienced radiologists at identifying cancer. This could mean that some people might not need to have a biopsy, which is an invasive procedure. The model also did well in other areas, such as identifying triple-negative breast cancer and predicting how well treatment will work. |
