Summary of Uncertainty Quantification in Detecting Choroidal Metastases on Mri Via Evolutionary Strategies, by Bala Mcrae-posani et al.
Uncertainty Quantification in Detecting Choroidal Metastases on MRI via Evolutionary Strategies
by Bala McRae-Posani, Andrei Holodny, Hrithwik Shalu, Joseph N Stember
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
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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 proposes a method for uncertainty quantification in small data AI approaches applied to radiology images. Specifically, it focuses on deep neuroevolution (DNE) for training a simple Convolutional Neural Network (CNN) for binary classification of MRI images of the eyes. The goal is to distinguish between normal eyes and those with choroidal metastases. The method addresses concerns around trustworthiness in AI applications in radiology by providing uncertainty quantification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study uses DNE to train a CNN on 36 images (18 with and 18 without tumors) for classification of MRI images of the eyes. It aims to detect choroidal metastases, which can be challenging due to limited annotated datasets in radiology. |
Keywords
* Artificial intelligence * Classification * Cnn * Neural network
