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20202023
most citedWeakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images

185 citations · 352 across the 19 of their papers we have counts for

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13 papers · 1 filter

cs.CV20222 cited

CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation

Ran Gu, Guotai Wang, Jiangshan Lu +8

Generalization to previously unseen images with potential domain shifts and different styles is essential for clinically applicable medical image segmentation, and the ability to d…

cs.CV20221 cited

Contrastive Domain Disentanglement for Generalizable Medical Image Segmentation

Ran Gu, Jiangshan Lu, Jingyang Zhang +4

Efficiently utilizing discriminative features is crucial for convolutional neural networks to achieve remarkable performance in medical image segmentation and is also important for…

cs.CV20211 cited

Multi-frame Collaboration for Effective Endoscopic Video Polyp Detection via Spatial-Temporal Feature Transformation

Lingyun Wu, Zhiqiang Hu, Yuanfeng Ji +2

Precise localization of polyp is crucial for early cancer screening in gastrointestinal endoscopy. Videos given by endoscopy bring both richer contextual information as well as mor…

cs.CV2021

Hybrid Supervision Learning for Pathology Whole Slide Image Classification

Jiahui Li, Wen Chen, Xiaodi Huang +5

Weak supervision learning on classification labels has demonstrated high performance in various tasks, while a few pixel-level fine annotations are also affordable. Naturally a que…

cs.CV202115 cited

Multi-Compound Transformer for Accurate Biomedical Image Segmentation

Yuanfeng Ji, Ruimao Zhang, Huijie Wang +4

The recent vision transformer(i.e.for image classification) learns non-local attentive interaction of different patch tokens. However, prior arts miss learning the cross-scale depe…

cs.CV202111 cited

Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation

Jinxi Xiang, Zhuowei Li, Wenji Wang +2

Deep learning has demonstrated significant improvements in medical image segmentation using a sufficiently large amount of training data with manual labels. Acquiring well-represen…