Summary of Exploring Intrinsic Properties Of Medical Images For Self-supervised Binary Semantic Segmentation, by Pranav Singh and Jacopo Cirrone
Exploring Intrinsic Properties of Medical Images for Self-Supervised Binary Semantic Segmentation
by Pranav Singh, Jacopo Cirrone
First submitted to arxiv on: 4 Feb 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 paper introduces Medical imaging Enhanced with Dynamic Self-Adaptive Semantic Segmentation (MedSASS), a novel self-supervised framework tailored for medical image segmentation tasks. Building on recent advancements in self-supervised learning, MedSASS leverages unlabeled data to learn beneficial priors and outperforms existing state-of-the-art methods across four diverse medical datasets by 3.83%. The framework demonstrates significant improvements of 14.4% for CNN-based models and 6% for ViT-based architectures when trained end-to-end. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical image segmentation is a complex task that requires accurate identification of different structures within images. This paper presents a new approach called MedSASS, which uses self-supervised learning to improve the results. Self-supervised learning is a technique where AI models learn from data without being explicitly labeled by humans. In this case, MedSASS learns to segment medical images by looking at unlabeled data and identifying patterns that help it get better over time. |
Keywords
» Artificial intelligence » Cnn » Image segmentation » Self supervised » Semantic segmentation » Vit
