Summary of Biofusionnet: Deep Learning-based Survival Risk Stratification in Er+ Breast Cancer Through Multifeature and Multimodal Data Fusion, by Raktim Kumar Mondol et al.
BioFusionNet: Deep Learning-Based Survival Risk Stratification in ER+ Breast Cancer Through Multifeature and Multimodal Data Fusion
by Raktim Kumar Mondol, Ewan K.A. Millar, Arcot Sowmya, Erik Meijering
First submitted to arxiv on: 16 Feb 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 BioFusionNet framework is a deep learning model that combines image-derived features with genetic and clinical data to predict survival risk stratification for ER+ breast cancer patients. The model employs multiple self-supervised feature extractors pretrained on histopathological patches, which are then fused by a variational autoencoder and fed into a self-attention network generating patient-level features. A co-dual-cross-attention mechanism combines the histopathological features with genetic data, while clinical data is incorporated using a feed-forward network. The model also introduces a weighted Cox loss function to handle imbalanced survival data. BioFusionNet outperforms state-of-the-art methods, achieving a mean concordance index of 0.77 and a time-dependent area under the curve of 0.84. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BioFusionNet is a new way to predict how well people with breast cancer will do after treatment. The model uses pictures of cancer cells, genetic information, and other details about each patient to make a personalized prediction. This can help doctors choose the best treatment for each person. BioFusionNet is better than other methods at predicting survival rates and may one day help doctors give more effective care to breast cancer patients. |
Keywords
» Artificial intelligence » Cross attention » Deep learning » Loss function » Self attention » Self supervised » Variational autoencoder
