Publications (5)
ADFA: Attention-augmented Differentiable top-k Feature Adaptation for Unsupervised Medical Anomaly Detection
Yiming Huang, Guole Liu, Yaoru Luo +1
The scarcity of annotated data, particularly for rare diseases, limits the variability of training data and the range of detectable lesions, presenting a significant challenge for…
Elucidating Meta-Structures of Noisy Labels in Semantic Segmentation by Deep Neural Networks
Yaoru Luo, Guole Liu, Yuanhao Guo +1
Supervised training of deep neural networks (DNNs) by noisy labels has been studied extensively in image classification but much less in image segmentation. Our understanding of th…
Representing Topological Self-Similarity Using Fractal Feature Maps for Accurate Segmentation of Tubular Structures
Jiaxing Huang, Yanfeng Zhou, Yaoru Luo +3
Accurate segmentation of long and thin tubular structures is required in a wide variety of areas such as biology, medicine, and remote sensing. The complex topology and geometry of…
Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation
Yaoru Luo, Guole Liu, Yuanhao Guo +1
How deep neural networks (DNNs) learn from noisy labels has been studied extensively in image classification but much less in image segmentation. So far, our understanding of the l…
Advancing biological super-resolution microscopy through deep learning: a brief review
Tianjie Yang, Yaoru Luo, Wei Ji +1
Super-resolution microscopy overcomes the diffraction limit of conventional light microscopy in spatial resolution. By providing novel spatial or spatio-temporal information on bio…