32 citations · 36 across the 5 of their papers we have counts for
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WDA-Net: Weakly-Supervised Domain Adaptive Segmentation of Electron Microscopy
Dafei Qiu, Jiajin Yi, Jialin Peng
Accurate segmentation of organelle instances, e.g., mitochondria, is essential for electron microscopy analysis. Despite the outstanding performance of fully supervised methods, th…
Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models
Jialin Peng, Ye Wang
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 unav…
HIVE-Net: Centerline-Aware HIerarchical View-Ensemble Convolutional Network for Mitochondria Segmentation in EM Images
Zhimin Yuan, Xiaofen Ma, Jiajin Yi +2
Semantic segmentation of electron microscopy (EM) is an essential step to efficiently obtain reliable morphological statistics. Despite the great success achieved using deep convol…
Adversarial-Prediction Guided Multi-task Adaptation for Semantic Segmentation of Electron Microscopy Images
Jiajin Yi, Zhimin Yuan, Jialin Peng
Semantic segmentation is an essential step for electron microscopy (EM) image analysis. Although supervised models have achieved significant progress, the need for labor intensive…
EM-NET: Centerline-Aware Mitochondria Segmentation in EM Images via Hierarchical View-Ensemble Convolutional Network
Zhimin Yuan, Jiajin Yi, Zhengrong Luo +2
Although deep encoder-decoder networks have achieved astonishing performance for mitochondria segmentation from electron microscopy (EM) images, they still produce coarse segmentat…