Summary of Drim: Learning Disentangled Representations From Incomplete Multimodal Healthcare Data, by Lucas Robinet et al.
DRIM: Learning Disentangled Representations from Incomplete Multimodal Healthcare Data
by Lucas Robinet, Ahmad Berjaoui, Ziad Kheil, Elizabeth Cohen-Jonathan Moyal
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This paper presents a novel deep learning method called DRIM for integrating multimodal medical data, including histopathology slides, MRI, and genetic data. The goal is to improve prognosis prediction and unveil new treatment pathways. Contrastive learning is used to derive representations from paired data, but this approach assumes that different views contain the same task-relevant information, which can be restrictive when handling medical data. DRIM captures shared and unique representations despite data sparsity by encoding a representation for each modality into two components: patient-related information common across modalities and modality-specific details. This is achieved by increasing shared information among different patient modalities while minimizing overlap between shared and unique components within each modality. The method outperforms state-of-the-art algorithms on glioma patients’ survival prediction tasks, showing robustness to missing modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to use medical data from different sources, like pictures of tumors or patient test results. Doctors want to make better predictions about how well someone will do and find new ways to treat diseases. Right now, machines have trouble combining this information because it’s very different. The researchers created a new method called DRIM that can handle all these types of data at once. It’s like taking a picture from multiple angles and then putting all the pieces together to get a complete view. This helps doctors make more accurate predictions about patient survival rates. |
Keywords
» Artificial intelligence » Deep learning
