Summary of Quantitative Evaluation Of the Saliency Map For Alzheimer’s Disease Classifier with Anatomical Segmentation, by Yihan Zhang et al.
Quantitative Evaluation of the Saliency Map for Alzheimer’s Disease Classifier with Anatomical Segmentation
by Yihan Zhang, Xuanshuo Zhang, Wei Wu, Haohan Wang
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
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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 This paper addresses a critical limitation in using saliency maps for Alzheimer’s disease diagnosis. By allocating saliency values to different brain regions, the authors gain a comprehensive understanding of deep learning classifier decisions. A new evaluation metric, Brain Volume Change Score (VCS), is introduced to relate model performance to brain volume changes in patients with Alzheimer’s disease. The VCS is computed by correlating brain volume changes and saliency values across different brain regions. The authors train candidate models on the ADNI dataset and test them on three datasets. Results show that models with higher VCS scores produce more detailed saliency maps relevant to Alzheimer’s pathology, and gradient-based adversarial training strategies like FGSM and stochastic masking can improve model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a problem in using special maps to understand how computers diagnose Alzheimer’s disease. The maps show which parts of the brain are important for diagnosis. But, Alzheimer’s is a complex disease that affects different people differently. So, it’s hard to understand what these maps mean without knowing more about each person’s brain changes over time. To solve this problem, the authors create a new way to measure how well the computers do at diagnosing Alzheimer’s based on how well they match brain volume changes with the map values. They train and test their computers using four different datasets. The results show that better computers produce maps that are more relevant to Alzheimer’s and can be improved by special training techniques. |
Keywords
* Artificial intelligence * Deep learning
