Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation

Nuclei instance segmentation on histopathology images is of great clinical value for disease analysis. Generally, fully-supervised algorithms for this task require pixel-wise manual annotations, which is especially time-consuming and laborious for the high nuclei density. To alleviate the annotation...

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Veröffentlicht in:	IEEE transactions on medical imaging 2023-10, Vol.PP (10), p.1-1
Hauptverfasser:	Zhou, Yang, Wu, Yongjian, Wang, Zihua, Wei, Bingzheng, Lai, Maode, Shou, Jianzhong, Fan, Yubo, Xu, Yan
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Annotations Cancer Classifiers CNN Interpretability Convolutional neural networks Deep learning Histopathology Image annotation Image processing Image segmentation Image-level Annotation Information processing Instance segmentation Labels Machine learning Masks Multi-task Learning Multitasking Nuclei Nuclei Segmentation Semi-supervised Learning Supervised learning Task analysis Weakly Supervised Learning
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