Search Results for author:
Found 9 papers, 1 papers with code
StegaNeRF: Embedding Invisible Information within Neural Radiance Fields
Recent advances in neural rendering imply a future of widespread visual data distributions through sharing NeRF model weights.
Hint-dynamic Knowledge Distillation
Knowledge Distillation (KD) transfers the knowledge from a high-capacity teacher model to promote a smaller student model.
Knowledge Condensation Distillation
Knowledge Distillation (KD) transfers the knowledge from a high-capacity teacher network to strengthen a smaller student.
AFSC: Adaptive Fourier Space Compression for Anomaly Detection
Data augmentation based methods construct pseudo-healthy images by "pasting" fake lesions on real healthy ones, and a network is trained to predict healthy images in a supervised manner.
Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation
In this paper, we propose a novel DG scheme of episodic training with task augmentation on medical imaging classification.
Hierarchical Deep Network with Uncertainty-aware Semi-supervised Learning for Vessel Segmentation
In this paper, to address the above issues, we propose a hierarchical deep network where an attention mechanism localizes the low-contrast capillary regions guided by the whole vessels, and enhance the spatial activation in those areas for the sub-type vessels.
Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translation
In this paper, to alleviate this issue, we introduce the semantic space of healthy anatomy in the process of modeling healthy-data distribution.
Consistent Posterior Distributions under Vessel-Mixing: A Regularization for Cross-Domain Retinal Artery/Vein Classification
In this paper, we propose a novel vessel-mixing based consistency regularization framework, for cross-domain learning in retinal A/V classification.
Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding
Motivated by the spatial consistency and regularity in medical images, we developed an efficient global correlation module to capture the correlation between a support and query image and incorporate it into the deep network called global correlation network.
