Few-Shot Breast Cancer Metastases Classification via Unsupervised Cell Ranking

Tumor metastases detection is of great importance for the treatment of breast cancer patients. Various CNN (convolutional neural network) based methods get excellent performance in object detection/segmentation. However, the detection of metastases in hematoxylin and eosin (H&E) stained whole-sl...

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Veröffentlicht in:	IEEE/ACM transactions on computational biology and bioinformatics 2021-09, Vol.18 (5), p.1914-1923
Hauptverfasser:	Chen, Jiaojiao, Jiao, Jianbo, He, Shengfeng, Han, Guoqiang, Qin, Jing
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial neural networks Breast cancer Breast Neoplasms - diagnostic imaging Breast Neoplasms - pathology Cell Count - methods Cell number Compression Feature extraction Female Few-shot learning Histocytochemistry Humans Image classification Image compression Image Interpretation, Computer-Assisted - methods Image segmentation Labels Learning Lymph Nodes - diagnostic imaging Lymph Nodes - pathology Machine learning Medical imaging Metastases metastases classification Metastasis Neoplasm Metastasis - diagnostic imaging Neoplasm Metastasis - pathology Neural networks Neural Networks, Computer Object recognition Pathology Ranking Source code Task analysis Training data Tumor cells Tumors unsupervised learning Unsupervised Machine Learning
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