9 citations · 20 across the 16 of their papers we have counts for
7 papers · 1 filter
Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning
Ruining Deng, Yanwei Li, Peize Li +11
Multi-class cell segmentation in high-resolution Giga-pixel whole slide images (WSI) is critical for various clinical applications. Training such an AI model typically requires lab…
An Accelerated Pipeline for Multi-label Renal Pathology Image Segmentation at the Whole Slide Image Level
Haoju Leng, Ruining Deng, Zuhayr Asad +4
Deep-learning techniques have been used widely to alleviate the labour-intensive and time-consuming manual annotation required for pixel-level tissue characterization. Our previous…
Omni-Seg: A Scale-aware Dynamic Network for Renal Pathological Image Segmentation
Ruining Deng, Quan Liu, Can Cui +9
Comprehensive semantic segmentation on renal pathological images is challenging due to the heterogeneous scales of the objects. For example, on a whole slide image (WSI), the cross…
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…
MAg: a simple learning-based patient-level aggregation method for detecting microsatellite instability from whole-slide images
Kaifeng Pang, Zuhayr Asad, Shilin Zhao +1
The prediction of microsatellite instability (MSI) and microsatellite stability (MSS) is essential in predicting both the treatment response and prognosis of gastrointestinal cance…
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…