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Found 8 papers, 1 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.
REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening
Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge -- Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2).
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.
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.
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.
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.
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.
