Publications (12)
Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial Networks
Bo Hu, Ye Tang, Eric I-Chao Chang +3
The visual attributes of cells, such as the nuclear morphology and chromatin openness, are critical for histopathology image analysis. By learning cell-level visual representation,…
Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation
Yang Zhou, Yongjian Wu, Zihua Wang +5
Nuclei instance segmentation on histopathology images is of great clinical value for disease analysis. Generally, fully-supervised algorithms for this task require pixel-wise manua…
SDPT: Synchronous Dual Prompt Tuning for Fusion-based Visual-Language Pre-trained Models
Yang Zhou, Yongjian Wu, Jiya Saiyin +4
Prompt tuning methods have achieved remarkable success in parameter-efficient fine-tuning on large pre-trained models. However, their application to dual-modal fusion-based visual-…
Gland Instance Segmentation by Deep Multichannel Side Supervision
Yan Xu, Yang Li, Mingyuan Liu +3
In this paper, we propose a new image instance segmentation method that segments individual glands (instances) in colon histology images. This is a task called instance segmentatio…
Nucleus-aware Self-supervised Pretraining Using Unpaired Image-to-image Translation for Histopathology Images
Zhiyun Song, Penghui Du, Junpeng Yan +5
Self-supervised pretraining attempts to enhance model performance by obtaining effective features from unlabeled data, and has demonstrated its effectiveness in the field of histop…
AttriPrompter: Auto-Prompting with Attribute Semantics for Zero-shot Nuclei Detection via Visual-Language Pre-trained Models
Yongjian Wu, Yang Zhou, Jiya Saiyin +4
Large-scale visual-language pre-trained models (VLPMs) have demonstrated exceptional performance in downstream object detection through text prompts for natural scenes. However, th…