Summary of A Multimodal Object-level Contrast Learning Method For Cancer Survival Risk Prediction, by Zekang Yang et al.
A Multimodal Object-level Contrast Learning Method for Cancer Survival Risk Prediction
by Zekang Yang, Hong Liu, Xiangdong Wang
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 A novel approach to computer-aided cancer survival risk prediction is proposed in this paper, focusing on a weakly supervised ordinal regression task. The challenge lies in incorporating multiple clinical factors, including pathological images and genomic data. To address this, the authors introduce multimodal object-level contrast learning for training a survival risk predictor. This method constructs contrast learning pairs based on the survival risk relationship among samples, then applies cross-modal contrast to extend it to the multimodal scenario. The authors also develop attention-based and self-normalizing neural networks to account for the heterogeneity of pathological images and genomics data. The proposed approach outperforms state-of-the-art methods on two public multimodal cancer datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new way to predict the risk of death from cancer using computer algorithms. By combining different types of medical data, such as pictures of tumors and genetic information, researchers can create a more accurate predictor of patient outcomes. The approach uses a technique called contrast learning, which helps the algorithm learn what makes certain patients more or less likely to survive. This method is tested on two large datasets of cancer patients and shows better results than current methods. |
Keywords
» Artificial intelligence » Attention » Regression » Supervised
