4 citations · 5 across the 6 of their papers we have counts for
6 papers
Towards Effective and Efficient Context-aware Nucleus Detection in Histopathology Whole Slide Images
Zhongyi Shui, Honglin Li, Yunlong Zhang +7
Nucleus detection in histopathology whole slide images (WSIs) is crucial for a broad spectrum of clinical applications. The gigapixel size of WSIs necessitates the use of sliding w…
Long-MIL: Scaling Long Contextual Multiple Instance Learning for Histopathology Whole Slide Image Analysis
Honglin Li, Yunlong Zhang, Chenglu Zhu +3
Histopathology image analysis is the golden standard of clinical diagnosis for Cancers. In doctors daily routine and computer-aided diagnosis, the Whole Slide Image (WSI) of histop…
Exploring Unsupervised Cell Recognition with Prior Self-activation Maps
Pingyi Chen, Chenglu Zhu, Zhongyi Shui +4
The success of supervised deep learning models on cell recognition tasks relies on detailed annotations. Many previous works have managed to reduce the dependency on labels. Howeve…
Unsupervised Dense Nuclei Detection and Segmentation with Prior Self-activation Map For Histology Images
Pingyi Chen, Chenglu Zhu, Zhongyi Shui +4
The success of supervised deep learning models in medical image segmentation relies on detailed annotations. However, labor-intensive manual labeling is costly and inefficient, esp…
Weakly Supervised Learning for cell recognition in immunohistochemical cytoplasm staining images
Shichuan Zhang, Chenglu Zhu, Honglin Li +2
Cell classification and counting in immunohistochemical cytoplasm staining images play a pivotal role in cancer diagnosis. Weakly supervised learning is a potential method to deal…
Generalizing Nucleus Recognition Model in Multi-source Images via Pruning
Jiatong Cai, Chenglu Zhu, Can Cui +4
Ki67 is a significant biomarker in the diagnosis and prognosis of cancer, whose index can be evaluated by quantifying its expression in Ki67 immunohistochemistry (IHC) stained imag…