Few-shot breast cancer metastases classification via unsupervised cell ranking

Jiaojiao Chen, Jianbo Jiao, Shengfeng He, Guoqiang Han, Jing Qin

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
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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-slide images (WSI) is still challenging mainly due to two aspects. (1) The resolution of the image is too large. (2) lacking labeled training data. Whole-slide images generally stored in a multi-resolution structure with multiple downsampled tiles. It is difficult to feed the whole image into memory without compression. Moreover, labeling images for the pathologists are time-consuming and expensive. In this paper, we study the problem of detecting breast cancer metastases in the pathological image on patch level. To address the abovementioned challenges, we propose a few-shot learning method to classify whether an image patch contains tumor cells. Specifically, we propose a patch-level unsupervised cell ranking approach, which only relies on images with limited labels. The main idea of the proposed method is that when cropping a patch A from the WSI and further cropping a sub-patch B from A, the cell number of A is always larger than that of B. Based on this observation, we make use of the unlabeled images to learn the ranking information of cell counting to extract the abstract features. Experimental results show that our method is effective to improve the patch-level classification accuracy, compared to the traditional supervised method. The source code is publicly available at https://github.com/fewshot-camelyon.
Original languageEnglish
Pages (from-to)1914-1923
Number of pages10
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number5
Early online date16 Dec 2019
Publication statusPublished - Sept 2021

Bibliographical note

Funding Information:
This work was supported by the National Natural Science Foundation of China (No. 61472145, No. 61972162, and No. 61702194), a grant from the Hong Kong Research Grants Council (Project No. PolyU 152035/17E), the Special Fund of Science and Technology Research and Development on Application From Guangdong Province (SF-STRDA-GD) (No. 2016B010127003), the Guangzhou Key Industrial Technology Research fund (No. 201802010036), the Guangdong Natural Science Foundation (No. 2017A030312008), and the CCFTencentOpen Research fund (CCF-Tencent RAGR20190112).

Publisher Copyright:
© 2004-2012 IEEE.


  • Few-shot learning
  • metastases classification
  • unsupervised learning

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics


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