Summary of Fusion Of Diffusion Weighted Mri and Clinical Data For Predicting Functional Outcome After Acute Ischemic Stroke with Deep Contrastive Learning, by Chia-ling Tsai et al.
Fusion of Diffusion Weighted MRI and Clinical Data for Predicting Functional Outcome after Acute Ischemic Stroke with Deep Contrastive Learning
by Chia-Ling Tsai, Hui-Yun Su, Shen-Feng Sung, Wei-Yang Lin, Ying-Ying Su, Tzu-Hsien Yang, Man-Lin Mai
First submitted to arxiv on: 16 Feb 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 The proposed study aims to develop a deep fusion learning network that combines diffusion-weighted MRI modalities with structured health profiles to predict the functional outcome in patients with acute stroke. The network uses two-stage training, focusing on cross-modality representation learning and classification, with supervised contrastive learning to learn discriminative features. The input includes DWI, ADC images, and structured health profile data, with the goal of predicting whether a patient will require long-term care three months after the onset of stroke. The proposed fusion model outperforms existing models in the medical domain, achieving AUC, F1-score, and accuracy rates of 0.87, 0.80, and 80.45%, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study aims to develop a new way to predict how well people will recover after having a stroke. It combines special MRI scans with information about the patient’s health to make this prediction. The method uses two stages: first, it learns how to combine different types of data together, and then it uses that combined data to make predictions. This approach is tested on a large dataset of patients and shows promise in predicting whether someone will need long-term care after a stroke. |
Keywords
* Artificial intelligence * Auc * Classification * Diffusion * F1 score * Representation learning * Supervised
