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Prompt Learning With Bounding Box Constraints for Medical Image Segmentation
Mélanie Gaillochet
, Mehrdad Noori
, Sahar Dastani
,
Christian Desrosiers
,
Hervé Lombaert
École de technologie supérieure
Software and Information Technology Engineering Department
LIVIA - Imaging, Vision and Artificial Intelligence Laboratory
École de technologie supérieure
Université de Montréal
Polytechnique Montréal
Research output
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Journal Article
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peer-review
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Keyphrases
Medical Image Segmentation
100%
Bounding Box
100%
Foundation Models
100%
Prompt Learning
100%
Box Constraints
100%
Unsupervised Learning
50%
Supervised Approach
50%
Learning Approaches
25%
Segmentation Mask
25%
Optimization Scheme
25%
Pixel-wise
25%
User Intervention
25%
Segmentation Performance
25%
Weakly Supervised Segmentation
25%
Dice Score
25%
Fully-supervised
25%
Pseudo Label
25%
Medical Domain
25%
Downstream Task
25%
Bounding Box Annotation
25%
Multimodal Data
25%
Segment Anything Model
25%
Multiple Constraints
25%
Representational Power
25%
Weakly Supervised Method
25%
Annotation Efficiency
25%
Computer Science
Image Segmentation
100%
Annotation
100%
Prompt Learning
100%
Foundation Model
100%
Learning Approach
25%
Supervised Method
25%
Segmentation Performance
25%
Medical Domain
25%
Segment Anything Model
25%