Summary of Mm-survnet: Deep Learning-based Survival Risk Stratification in Breast Cancer Through Multimodal Data Fusion, by Raktim Kumar Mondol et al.
MM-SurvNet: Deep Learning-Based Survival Risk Stratification in Breast Cancer Through Multimodal Data Fusion
by Raktim Kumar Mondol, Ewan K.A. Millar, Arcot Sowmya, Erik Meijering
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 deep learning approach integrates histopathological imaging, genetic, and clinical data for survival risk stratification in breast cancer management. The method employs vision transformers like MaxViT for image feature extraction and self-attention for capturing intricate relationships. A dual cross-attention mechanism fuses these features with genetic data, while clinical data is incorporated at the final layer to enhance predictive accuracy. Experiments on the TCGA-BRCA dataset demonstrate superior performance with a mean C-index of 0.64, surpassing existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re developing a new way to help doctors decide what treatment is best for breast cancer patients. We’re combining information from medical images, genetic tests, and patient records using special computer algorithms. This helps us predict how likely it is that the cancer will come back or spread. Our approach does better than other methods at making these predictions, which can lead to more personalized and effective treatment plans. |
Keywords
» Artificial intelligence » Cross attention » Deep learning » Feature extraction » Self attention
