Summary of Multi-label Continual Learning For the Medical Domain: a Novel Benchmark, by Marina Ceccon et al.
Multi-Label Continual Learning for the Medical Domain: A Novel Benchmark
by Marina Ceccon, Davide Dalle Pezze, Alessandro Fabris, Gian Antonio Susto
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers address the limitations of current Continual Learning (CL) techniques by proposing a novel benchmark for medical imaging. The New Instances and New Classes (NIC) scenario combines class arrivals and domain shifts to model real-world applications. To tackle challenges like task inference, the authors propose Replay Consolidation with Label Propagation (RCLP), which outperforms existing approaches while minimizing forgetting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new benchmark for medical imaging that simulates real-world scenarios by introducing both new classes and domain shifts. The goal is to create a realistic setting for multi-label classification in medical images. To address common challenges like task inference, the authors propose a novel approach called Replay Consolidation with Label Propagation (RCLP). This method surpasses existing approaches while minimizing forgetting. |
Keywords
» Artificial intelligence » Classification » Continual learning » Inference
