Summary of Fess Loss: Feature-enhanced Spatial Segmentation Loss For Optimizing Medical Image Analysis, by Charulkumar Chodvadiya et al.
FESS Loss: Feature-Enhanced Spatial Segmentation Loss for Optimizing Medical Image Analysis
by Charulkumar Chodvadiya, Navyansh Mahla, Kinshuk Gaurav Singh, Kshitij Sharad Jadhav
First submitted to arxiv on: 13 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 This paper proposes Feature-Enhanced Spatial Segmentation Loss (FESS Loss) for medical image segmentation. The traditional methods face challenges in balancing spatial precision and comprehensive feature representation due to their reliance on traditional loss functions. FESS Loss integrates contrastive learning, which extracts intricate features, with Dice loss, ensuring both spatial accuracy and refined feature-based representation. This novel approach offers improved segmentation performance, particularly in scenarios with limited annotated data availability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical image segmentation is important for diagnosis, treatment, and research. The goal of this study is to improve the accuracy of medical image segmentation by combining two techniques: contrastive learning and Dice loss. The new method, called FESS Loss, can extract detailed features from images while also providing accurate spatial information. This makes it a useful tool for analyzing medical images. |
Keywords
» Artificial intelligence » Image segmentation » Precision
