Summary of Boosting Few-shot Learning with Disentangled Self-supervised Learning and Meta-learning For Medical Image Classification, by Eva Pachetti et al.
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification
by Eva Pachetti, Sotirios A. Tsaftaris, Sara Colantonio
First submitted to arxiv on: 26 Mar 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 proposed method aims to improve the performance and generalization capabilities of deep learning models trained in low-data regimes, a critical challenge in medical imaging applications. The strategy involves pre-training features through self-supervised learning, followed by meta-fine-tuning that leverages related classes between meta-training and meta-testing phases. This approach is tested on two distinct medical tasks: classifying prostate cancer aggressiveness from MRI data and breast cancer malignity from microscopic images. Results show superior performance compared to ablation experiments, maintaining competitiveness even in the presence of a distribution shift. The proposed method demonstrates effectiveness and wide applicability, offering another solution for addressing learning issues in data-scarce imaging domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a way to make deep learning models better at handling limited training data in medical imaging tasks like classifying prostate cancer or breast cancer from images. They do this by first pre-training the model using self-supervised learning, then fine-tuning it with more specific information about the task. This approach is tested on two different types of medical images and shows that it can work well even when there’s a difference between the training and testing data. |
Keywords
» Artificial intelligence » Deep learning » Fine tuning » Generalization » Self supervised
