Summary of Uncertainty-guided Annotation Enhances Segmentation with the Human-in-the-loop, by Nadieh Khalili and Joey Spronck and Francesco Ciompi and Jeroen Van Der Laak and Geert Litjens
Uncertainty-guided annotation enhances segmentation with the human-in-the-loop
by Nadieh Khalili, Joey Spronck, Francesco Ciompi, Jeroen van der Laak, Geert Litjens
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 A novel deep learning framework, Uncertainty-Guided Annotation (UGA), is proposed to address the limitations of traditional black-box models in clinical applications. UGA introduces a human-in-the-loop approach that enables AI models to convey their uncertainties to clinicians, acting as an automated quality control mechanism. By quantifying uncertainty at the pixel level, UGA reveals the model’s limitations and opens the door for clinician-guided corrections. The framework is evaluated on the Camelyon dataset for lymph node metastasis segmentation, demonstrating improved performance with added patches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning algorithms can be tricky to understand, but researchers have developed a new way to make them more transparent. This approach, called Uncertainty-Guided Annotation (UGA), helps doctors work together with AI machines to improve medical image analysis. UGA shows where the AI is unsure about its results, allowing doctors to correct mistakes and make better diagnoses. The technique was tested on images of lymph nodes and showed promising results. |
Keywords
» Artificial intelligence » Deep learning
