Summary of Data Alchemy: Mitigating Cross-site Model Variability Through Test Time Data Calibration, by Abhijeet Parida et al.
Data Alchemy: Mitigating Cross-Site Model Variability Through Test Time Data Calibration
by Abhijeet Parida, Antonia Alomar, Zhifan Jiang, Pooneh Roshanitabrizi, Austin Tapp, Maria Ledesma-Carbayo, Ziyue Xu, Syed Muhammed Anwar, Marius George Linguraru, Holger R. Roth
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 proposed Data Alchemy method is an explainable stain normalization technique combined with test-time data calibration, which overcomes barriers in cross-site analysis for histopathology. This approach handles inherent domain shifts and minimizes them without changing the weights of the normalization or classifier networks. The framework extends to unseen sites in various clinical settings where data domain discrepancies are unknown. Extensive experiments demonstrate the efficacy of Data Alchemy in tumor classification, boosting the area under the precision-recall curve (AUPR) by 0.165 and improving classification performance further to 0.852. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Data Alchemy is a new way to make medical images work better across different hospitals and clinics. Right now, it’s hard to compare images taken at different sites because they might look different. The team came up with a solution that helps normalize these images so they can be compared more easily. This means doctors can use the same deep learning tools for diagnosis no matter where the patient is from. The new method works well and could make precision medicine more accessible by reducing the need for extra training or adjustments. |
Keywords
* Artificial intelligence * Boosting * Classification * Deep learning * Precision * Recall
