Summary of Label Sharing Incremental Learning Framework For Independent Multi-label Segmentation Tasks, by Deepa Anand et al.
Label Sharing Incremental Learning Framework for Independent Multi-Label Segmentation Tasks
by Deepa Anand, Bipul Das, Vyshnav Dangeti, Antony Jerald, Rakesh Mullick, Uday Patil, Pakhi Sharma, Prasad Sudhakar
First submitted to arxiv on: 17 Nov 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 The novel label sharing framework proposed in this paper transforms multiple datasets with disparate label sets into a single large dataset with shared labels, allowing for a single model to address all segmentation tasks. This eliminates the need for task-specific adaptations in network architectures and results in parameter- and data-efficient models. The framework is naturally amenable to incremental learning, where segmentations for new datasets can be easily learned. Experiments on various medical image segmentation datasets demonstrate the efficacy of the proposed method in terms of performance and incremental learning ability compared to alternative methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to build segmentation models for multiple datasets with different label sets. Instead of making separate models for each dataset, it uses a single shared model that can work on all datasets at once. This makes the models more efficient and easier to update as new data comes in. The method is tested on several medical image datasets and shows good results. |
Keywords
» Artificial intelligence » Image segmentation
