Summary of Improving Colorectal Cancer Screening and Risk Assessment Through Predictive Modeling on Medical Images and Records, by Shuai Jiang et al.
Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records
by Shuai Jiang, Christina Robinson, Joseph Anderson, William Hisey, Lynn Butterly, Arief Suriawinata, Saeed Hassanpour
First submitted to arxiv on: 13 Oct 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 A novel machine learning approach is presented that combines computer vision techniques with digital pathology and medical records to improve 5-year colorectal cancer (CRC) risk prediction. The study adapts a transformer-based model for histopathology image analysis, leveraging the New Hampshire Colonoscopy Registry’s dataset. Various multimodal fusion techniques are investigated, combining medical record information with deep learning-derived risk estimates. The results show that training a transformer model to predict intermediate clinical variables enhances 5-year CRC risk prediction performance, achieving an AUC of 0.630 compared to direct prediction. Furthermore, the fusion of imaging and non-imaging features demonstrates improved predictive capabilities for 5-year CRC risk. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to use computers to help doctors predict who is at risk for colon cancer is being developed. The approach combines information from medical records, digital pictures of colon tissue, and special computer algorithms. This helps make a more accurate prediction of who might get colon cancer in the next 5 years. The study uses a lot of data from a registry that tracks people’s colonoscopies. By combining different types of information, the computer can make better predictions than just looking at one thing alone. |
Keywords
» Artificial intelligence » Auc » Deep learning » Machine learning » Transformer
