8 citations · 15 across the 8 of their papers we have counts for
10 papers
CircleSnake: Instance Segmentation with Circle Representation
Ethan H. Nguyen, Haichun Yang, Zuhayr Asad +3
Circle representation has recently been introduced as a medical imaging optimized representation for more effective instance object detection on ball-shaped medical objects. With i…
Holistic Fine-grained GGS Characterization: From Detection to Unbalanced Classification
Yuzhe Lu, Haichun Yang, Zuhayr Asad +5
Recent studies have demonstrated the diagnostic and prognostic values of global glomerulosclerosis (GGS) in IgA nephropathy, aging, and end-stage renal disease. However, the fine-g…
Compound Figure Separation of Biomedical Images with Side Loss
Tianyuan Yao, Chang Qu, Quan Liu +11
Unsupervised learning algorithms (e.g., self-supervised learning, auto-encoder, contrastive learning) allow deep learning models to learn effective image representations from large…
BEDS: Bagging ensemble deep segmentation for nucleus segmentation with testing stage stain augmentation
Xing Li, Haichun Yang, Jiaxin He +5
Reducing outcome variance is an essential task in deep learning based medical image analysis. Bootstrap aggregating, also known as bagging, is a canonical ensemble algorithm for ag…
Improve Global Glomerulosclerosis Classification with Imbalanced Data using CircleMix Augmentation
Yuzhe Lu, Haichun Yang, Zheyu Zhu +3
The classification of glomerular lesions is a routine and essential task in renal pathology. Recently, machine learning approaches, especially deep learning algorithms, have been u…
EasierPath: An Open-source Tool for Human-in-the-loop Deep Learning of Renal Pathology
Zheyu Zhu, Yuzhe Lu, Ruining Deng +3
Considerable morphological phenotyping studies in nephrology have emerged in the past few years, aiming to discover hidden regularities between clinical and imaging phenotypes. Suc…