Summary of Reducing Self-supervised Learning Complexity Improves Weakly-supervised Classification Performance in Computational Pathology, by Tim Lenz et al.
Reducing self-supervised learning complexity improves weakly-supervised classification performance in computational pathology
by Tim Lenz, Omar S. M. El Nahhas, Marta Ligero, Jakob Nikolas Kather
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 investigates the application of self-supervised learning (SSL) methods in computational pathology to extract clinically actionable insights from routinely available histology data. The authors utilize consumer-grade hardware to analyze the effects of adaptations in data volume, architecture, and algorithms on downstream classification tasks. They train breast cancer foundation models on a large public patient cohort and validate them on various downstream classification tasks in a weakly supervised manner on two external public patient cohorts. The results demonstrate that improving SSL training duration by 90% can lead to enhanced downstream classification performance while reducing computational resources. By proposing adaptations for utilizing SSL in non-resource abundant environments, this study aims to make advanced deep learning models more accessible to institutions with limited budgets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps computers learn from medical images without needing a lot of human help. The team wants to know if they can use cheap computers and small amounts of data to train these computers to recognize different types of breast cancer. They found that by changing how the computer learns, they can get better results in less time using fewer resources. This means that hospitals with limited budgets can now use these advanced computer models to help doctors diagnose breast cancer more accurately. |
Keywords
* Artificial intelligence * Classification * Deep learning * Self supervised * Supervised
