Summary of Improving Uncertainty-error Correspondence in Deep Bayesian Medical Image Segmentation, by Prerak Mody et al.
Improving Uncertainty-Error Correspondence in Deep Bayesian Medical Image Segmentation
by Prerak Mody, Nicolas F. Chaves-de-Plaza, Chinmay Rao, Eleftheria Astrenidou, Mischa de Ridder, Nienke Hoekstra, Klaus Hildebrandt, Marius Staring
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 approach to semi-automated quality assessment (QA) in medical image segmentation is presented, leveraging deep Bayesian uncertainty to recommend potentially erroneous regions. The traditional QA process involves manual contouring, followed by error detection and correction, which is time-consuming. By promoting uncertainty only in inaccurate regions, the proposed method aims to alleviate the burden of manual QA. This is achieved through training a FlipOut model with an Accuracy-vs-Uncertainty (AvU) loss function. Two radiotherapy body sites, head-and-neck CT and prostate MR scans, are used as datasets for evaluation. The performance of uncertainty heatmaps is assessed using Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves. Results show that the proposed method successfully suppresses uncertainty in accurate voxels while maintaining similar presence in inaccurate voxels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to check for mistakes in medical images, which are used to help doctors diagnose and treat patients. Right now, someone has to look at these images and correct any errors, which can be time-consuming. The new approach uses special computer models that can tell when an image is likely to have mistakes. This helps doctors focus on the parts of the image that might need attention. The researchers tested this method using two types of medical scans: head and neck scans and prostate scans. They found that their method works well, correctly identifying areas where mistakes are likely. |
Keywords
» Artificial intelligence » Attention » Image segmentation » Loss function » Precision » Recall
