Summary of Unleashing the Potential Of Vision-language Pre-training For 3d Zero-shot Lesion Segmentation Via Mask-attribute Alignment, by Yankai Jiang et al.
Unleashing the Potential of Vision-Language Pre-Training for 3D Zero-Shot Lesion Segmentation via Mask-Attribute Alignment
by Yankai Jiang, Wenhui Lei, Xiaofan Zhang, Shaoting Zhang
First submitted to arxiv on: 21 Oct 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 Malenia, a novel framework for zero-shot lesion segmentation in 3D CT scans, which improves compatibility between mask representations and associated elemental attributes. It presents a multi-scale approach to align fine-grained lesion features with disease-related textual representations, enhancing the transfer of image-level knowledge to pixel-level tasks. The authors also introduce a Cross-Modal Knowledge Injection module to mutually benefit visual and textual features, guiding segmentation results. Malenia is evaluated on three datasets and 12 lesion categories, demonstrating superior performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Malenia is a new way to recognize diseases from CT scans without needing any training data. It’s hard for computers to understand tiny changes in images that are important for identifying certain lesions. This paper introduces a system called Malenia that helps computers learn about these small differences by linking what they’ve learned before with new, unseen information. |
Keywords
» Artificial intelligence » Mask » Zero shot
