Cluster-Based Multiple Instance Learning for Whole Slide Image Classification
Zhong Haiqin, Zhao Cheng, Lei Baiying*, Wang Tianfu*
Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging,School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, Guangdong, China
Abstract：Pathological images are the gold standard for cancer examination. Fast and accurate classification of pathological images, especially whole slide images (WSI), helps medical doctors provide personalized treatment and prognosis assessment for patients. In recent years, multiple instance learning (MIL) has played an increasingly important role in WSI classification. However, due to the limited number of WSIs and the low proportion of positive areas, the existing MIL method based on attention mechanism may lead to overfitting, thus affecting the classification performance. To solve this problem, we proposed a new clustering-based MIL classification method. Specifically, this method divided each bag into multiple pseudo bags to increase the number of packages and let the network pay attention to more positive instances. Then, to solve the problem that a pseudo-bag is easy to be full of negative instances in the pseudo-bag division process, resulting in noise, this paper proposed a new pseudo-bag division method based on clustering. Finally, to obtain more accurate classification results, we conducted secondary learning on the learned pseudo-bag-level features to get the final bag-level features and achieve the final WSI classification. We conducted experiments on the Camelyon16 and TCGA-Lung datasets, which have 399 and 1 038 WSIs, respectively, with classification accuracies of 90.69% and 86.54%, and F1-scores of 90.20% and 86.52%. The experimental results showed that the proposed method could be appled to WSI classification effectively.
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