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Found 7 papers, 0 papers with code
Cross-model Mutual Learning for Exemplar-based Medical Image Segmentation
In this paper, we introduce a novel Cross-model Mutual learning framework for Exemplar-based Medical image Segmentation (CMEMS), which leverages two models to mutually excavate implicit information from unlabeled data at multiple granularities.
AKGNet: Attribute Knowledge-Guided Unsupervised Lung-Infected Area Segmentation
It can learn statistical information and capture spatial correlations between image and text attributes in the embedding space, iteratively refining the mask to enhance segmentation.
Efficient Low-Rank GNN Defense Against Structural Attacks
As a result, instead of using the dense adjacency matrix directly, ELR-GNN can learn a low-rank and sparse estimate of it in a simple, efficient and easy to optimize manner.
Exemplar-FreeSOLO: Enhancing Unsupervised Instance Segmentation With Exemplars
Moreover, a new exemplar embedding contrastive module is designed to enhance the discriminative capability of the segmentation model by exploiting the contrastive exemplar-based guidance knowledge in the embedding space.
Annotation by Clicks: A Point-Supervised Contrastive Variance Method for Medical Semantic Segmentation
The proposed method trains the base segmentation network by using a novel contrastive variance (CV) loss to exploit the unlabeled pixels and a partial cross-entropy loss on the labeled pixels.
Exemplar Learning for Medical Image Segmentation
Medical image annotation typically requires expert knowledge and hence incurs time-consuming and expensive data annotation costs.
Remember the Difference: Cross-Domain Few-Shot Semantic Segmentation via Meta-Memory Transfer
To remedy this problem, we propose an interesting and challenging cross-domain few-shot semantic segmentation task, where the training and test tasks perform on different domains.
