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Found 11 papers, 7 papers with code
Seeing the wood for the trees: a contrastive regularization method for the low-resource Knowledge Base Question Answering
Given a context knowledge base (KB) and a corresponding question, the Knowledge Base Question Answering task aims to retrieve correct answer entities from this KB.
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain Tumor
Magnetic resonance imaging (MRI) is commonly used for brain tumor segmentation, which is critical for patient evaluation and treatment planning.
How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Images
To evaluate the label-efficiency of our finetuning method, we compare the results of these three prediction heads on a public medical image segmentation dataset with limited labeled data.
Conditional Diffusion Models for Weakly Supervised Medical Image Segmentation
Recent advances in denoising diffusion probabilistic models have shown great success in image synthesis tasks.
Additional Positive Enables Better Representation Learning for Medical Images
This paper presents a new way to identify additional positive pairs for BYOL, a state-of-the-art (SOTA) self-supervised learning framework, to improve its representation learning ability.
Unsupervised Feature Clustering Improves Contrastive Representation Learning for Medical Image Segmentation
Self-supervised instance discrimination is an effective contrastive pretext task to learn feature representations and address limited medical image annotations.
Contrastive Image Synthesis and Self-supervised Feature Adaptation for Cross-Modality Biomedical Image Segmentation
As such, we enforce contrastive losses on the generated images and the input images to train the encoder of a segmentation model to minimize the discrepancy between paired images in the learned embedding space.
Context-guided Triple Matching for Multiple Choice Question Answering
The task of multiple choice question answering (MCQA) refers to identifying a suitable answer from multiple candidates, by estimating the matching score among the triple of the passage, question and answer.
Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation
With different amounts of labeled data, our methods consistently outperform the state-of-the-art contrast-based methods and other semi-supervised learning techniques.
Positional Contrastive Learning for Volumetric Medical Image Segmentation
The success of deep learning heavily depends on the availability of large labeled training sets.
Attention Based Joint Learning for Supervised Premature Ventricular Contraction Differentiation with Unsupervised Abnormal Beat Segmentation
Though the training data is only labeled to supervise theclassifier, the segmenter and the classifier are trained in an end-to-end manner sothat optimizing classification performance also adjusts how the abnormal beats aresegmented.
