Abstract
Whole slide images (WSIs) are high-resolution and lack localized annotations, whose classification can be treated as a multiple instance learning (MIL) problem while slide-level labels are available. We introduce a approach for WSI classification that leverages the MIL and Transformer, effectively eliminating the requirement for localized annotations. Our method consists of three key components. Firstly, we use ResNet50, which has been pre-trained on ImageNet, as an instance feature extractor. Secondly, we present a Transformer-based MIL aggregator that adeptly captures contextual information within individual regions and correlation information among diverse regions within the WSI. Thirdly, we introduce the global average pooling (GAP) layer to increase the mapping relationship between WSI features and category features. To evaluate our model, we conducted experiments on the The Cancer Imaging Archive (TCIA) Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset. Our proposed method achieves a top-1 accuracy of 94.8% and an area under the curve (AUC) exceeding 0.996, establishing state-of-the-art performance in WSI classification without reliance on localized annotations. The results demonstrate the superiority of our approach compared to previous MIL-based methods.
H. Luan and T. Hu—Equal contribution.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (grant numbers 92259101) and the Strategic Priority Research Program of the Chinese Academy of Sciences (grant number XDB38040100).
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The pathology slides and corresponding labels for WSIs are available from the CPTAC Pathology Portal. All source code used in our study was implemented in Python using PyTorch learning library, which are available at https://github.com/Luan-zb/TMG.
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Luan, H. et al. (2023). Multi-class Cancer Classification of Whole Slide Images Through Transformer and Multiple Instance Learning. In: Guo, X., Mangul, S., Patterson, M., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2023. Lecture Notes in Computer Science(), vol 14248. Springer, Singapore. https://doi.org/10.1007/978-981-99-7074-2_12
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