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Found 8 papers, 7 papers with code
Diversified and Personalized Multi-rater Medical Image Segmentation
To address it, the common practice is to gather multiple annotations from different experts, leading to the setting of multi-rater medical image segmentation.
Novel Scenes & Classes: Towards Adaptive Open-set Object Detection
Then, a high-order metric is proposed to match the most significant motif as high-order patterns, serving for motif-guided novel-class learning.
Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy Labels
Noisy labels collected with limited annotation cost prevent medical image segmentation algorithms from learning precise semantic correlations.
SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation
By exploiting computational geometry analysis and properties of segmentation, we design three complementary regularizers, i. e. volume regularization, anchor guidance, convex guarantee, to approximate the true SimT.
MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation
Existing self-training based UDA approaches assign pseudo labels for target data and treat them as ground truth labels to fully leverage unlabeled target data for model adaptation.
Ranked #30 on
Synthetic-to-Real Translation
on SYNTHIA-to-Cityscapes
Complementary Network with Adaptive Receptive Fields for Melanoma Segmentation
To tackle these issues, we propose a novel complementary network with adaptive receptive filed learning.
Domain Knowledge Based Brain Tumor Segmentation and Overall Survival Prediction
Automatically segmenting sub-regions of gliomas (necrosis, edema and enhancing tumor) and accurately predicting overall survival (OS) time from multimodal MRI sequences have important clinical significance in diagnosis, prognosis and treatment of gliomas.
Unsupervised End-to-end Learning for Deformable Medical Image Registration
The contributions of our algorithm are threefold: (1) We transplant traditional image registration algorithms to an end-to-end convolutional neural network framework, while maintaining the unsupervised nature of image registration problems.
