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Found 17 papers, 3 papers with code
Domain Adaptation Meets Zero-Shot Learning: An Annotation-Efficient Approach to Multi-Modality Medical Image Segmentation
Second, we propose a relation prototype awareness module to make the zero-shot model aware of information contained in the prototypes.
Label-efficient Hybrid-supervised Learning for Medical Image Segmentation
Due to the lack of expertise for medical image annotation, the investigation of label-efficient methodology for medical image segmentation becomes a heated topic.
REFUGE2 Challenge: Treasure for Multi-Domain Learning in Glaucoma Assessment
Glaucoma is the second leading cause of blindness and is the leading cause of irreversible blindness disease in the world.
Multi-Anchor Active Domain Adaptation for Semantic Segmentation
Unsupervised domain adaption has proven to be an effective approach for alleviating the intensive workload of manual annotation by aligning the synthetic source-domain data and the real-world target-domain samples.
A New Bidirectional Unsupervised Domain Adaptation Segmentation Framework
Domain shift happens in cross-domain scenarios commonly because of the wide gaps between different domains: when applying a deep learning model well-trained in one domain to another target domain, the model usually performs poorly.
Learning Calibrated Medical Image Segmentation via Multi-Rater Agreement Modeling
To our knowledge, our work is the first in producing calibrated predictions under different expertise levels for medical image segmentation.
Ensembled ResUnet for Anatomical Brain Barriers Segmentation
However, due to the visual and anatomical differences between different modalities, the accurate segmentation of brain structures becomes challenging.
TR-GAN: Topology Ranking GAN with Triplet Loss for Retinal Artery/Vein Classification
A topology ranking discriminator based on ordinal regression is proposed to rank the topological connectivity level of the ground-truth, the generated A/V mask and the intentionally shuffled mask.
Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling
However, when being used for model training, only the final ground-truth label is utilized, while the critical information contained in the raw multi-rater gradings regarding the image being an easy/hard case is discarded.
A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation
Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness among Asian people.
Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations
In deep learning era, pretrained models play an important role in medical image analysis, in which ImageNet pretraining has been widely adopted as the best way.
AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography
To address this, we organized the Angle closure Glaucoma Evaluation challenge (AGE), held in conjunction with MICCAI 2019.
A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment.
Identification of primary angle-closure on AS-OCT images with Convolutional Neural Networks
In this paper, we propose an efficient and accurate end-to-end architecture for angle-closure classification and scleral spur localization.
Uncertainty-Guided Domain Alignment for Layer Segmentation in OCT Images
Automatic and accurate segmentation for retinal and choroidal layers of Optical Coherence Tomography (OCT) is crucial for detection of various ocular diseases.
Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge
This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017.
Pyramid Network with Online Hard Example Mining for Accurate Left Atrium Segmentation
Accurately segmenting left atrium in MR volume can benefit the ablation procedure of atrial fibrillation.
