97 citations · 128 across the 18 of their papers we have counts for
6 papers · 1 filter
Omni-Seg: A Single Dynamic Network for Multi-label Renal Pathology Image Segmentation using Partially Labeled Data
Ruining Deng, Quan Liu, Can Cui +3
Computer-assisted quantitative analysis on Giga-pixel pathology images has provided a new avenue in histology examination. The innovations have been largely focused on cancer patho…
Circle Representation for Medical Object Detection
Ethan H. Nguyen, Haichun Yang, Ruining Deng +7
Box representation has been extensively used for object detection in computer vision. Such representation is efficacious but not necessarily optimized for biomedical objects (e.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…
SimTriplet: Simple Triplet Representation Learning with a Single GPU
Quan Liu, Peter C. Louis, Yuzhe Lu +9
Contrastive learning is a key technique of modern self-supervised learning. The broader accessibility of earlier approaches is hindered by the need of heavy computational resources…
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…