Few-shot breast cancer metastases classification via unsupervised cell ranking

Publication Type

Journal Article

Publication Date

9-2021

Abstract

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.

Keywords

Breast cancer, Pathology, Tumors, Machine learning, Training data, Task analysis, Few-shot learning, metastases classification, unsupervised learning

Discipline

Information Security

Research Areas

Information Systems and Management

Publication

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volume

18

Issue

5

First Page

1914

Last Page

1923

ISSN

1545-5963

Identifier

10.1109/TCBB.2019.2960019

Publisher

Association for Computing Machinery (ACM)

Additional URL

https://doi.org/10.1109/TCBB.2019.2960019

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