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Found 6 papers, 2 papers with code
Weakly-Supervised Cross-Domain Segmentation of Electron Microscopy with Sparse Point Annotation
To address these issues, we investigate a highly annotation-efficient weak supervision, which assumes only sparse center-points on a small subset of object instances in the target training images.
WDA-Net: Weakly-Supervised Domain Adaptive Segmentation of Electron Microscopy
Aiming to develop a highly annotation-efficient approach with competitive performance, we focus on weakly-supervised domain adaptation (WDA) with a type of extremely sparse and weak annotation demanding minimal annotation efforts, i. e., sparse point annotations on only a small subset of object instances.
Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models
Despite the remarkable performance of deep learning methods on various tasks, most cutting-edge models rely heavily on large-scale annotated training examples, which are often unavailable for clinical and health care tasks.
HIVE-Net: Centerline-Aware HIerarchical View-Ensemble Convolutional Network for Mitochondria Segmentation in EM Images
Semantic segmentation of electron microscopy (EM) is an essential step to efficiently obtain reliable morphological statistics.
Adversarial-Prediction Guided Multi-task Adaptation for Semantic Segmentation of Electron Microscopy Images
To complicate matters further, supervised learning models may not generalize well on a novel dataset due to domain shift.
EM-NET: Centerline-Aware Mitochondria Segmentation in EM Images via Hierarchical View-Ensemble Convolutional Network
To address these problems, we introduce a multi-task network named EM-Net, which includes an auxiliary centerline detection task to account for shape information of mitochondria represented by centerline.
