Summary of Saswise-ue: Segmentation and Synthesis with Interpretable Scalable Ensembles For Uncertainty Estimation, by Weijie Chen and Alan Mcmillan
SASWISE-UE: Segmentation and Synthesis with Interpretable Scalable Ensembles for Uncertainty Estimation
by Weijie Chen, Alan McMillan
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Methodology (stat.ME)
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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 paper introduces an ensemble framework for medical deep learning models, focusing on enhancing their interpretability and clinical applicability. The approach generates uncertainty maps, enabling end-users to evaluate the reliability of model outputs. A strategy is proposed to develop diverse models from a single well-trained checkpoint, involving output fusion and uncertainty estimation based on disagreements. The method is tested on CT body segmentation and MR-CT synthesis datasets using U-Net and UNETR models, achieving mean Dice coefficients of 0.814 in segmentation and Mean Absolute Errors of 88.17 HU in synthesis. The framework demonstrates robustness under corruption and undersampling, maintaining correlation between uncertainty and error. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to make medical deep learning models more useful for doctors. It does this by combining multiple predictions from different models into one final answer, while also showing how certain each prediction is. This helps doctors understand when the model’s answers are reliable or not. The method was tested on two types of medical image tasks: segmenting bodies and synthesizing MR-CT images. It did well on both tasks, achieving good results with and without noise in the data. |
Keywords
* Artificial intelligence * Deep learning
