SMILE: Sparse-Attention based Multiple Instance Contrastive Learning for Glioma Sub-Type Classification Using Pathological Images

Mengkang Lu, Yongsheng Pan, Dong Nie, Feihong Liu, Feng Shi, Yong Xia, Dinggang Shen
Proceedings of the MICCAI Workshop on Computational Pathology, PMLR 156:159-169, 2021.

Abstract

Gliomas are the most prevalent malignant brain tumor in adults and can be classified into four typical sub-types based on histological features. Histological diagnosis by pathologists via microscopic visual inspection of pathological slides has been the gold standard for glioma grading, especially hematoxylin and eosin (H&E) sections. However, due to spatial heterogeneity and complex tumor micro-environment, it is difficult and time-consuming for pathologists to differentiate glioma sub-types. In this paper, we propose a Sparse-attention based Multiple Instance contrastive LEarning (SMILE) method for glioma sub-type classification. First, we use contrastive learning to extract meaningful representations from pathological images. Second, we propose the sparse-attention multiple instance learning aggregator to get sparse instance representations in a bag for label prediction. We validate the proposed SMILE method using a glioma dataset from The Cancer Genome Atlas (TCGA). Experimental results show superior performance of our method over competing ones. Ablation study further demonstrates the effectiveness of our design of SMILE.

Cite this Paper

BibTeX
@InProceedings{pmlr-v156-lu21a, title = {SMILE: Sparse-Attention based Multiple Instance Contrastive Learning for Glioma Sub-Type Classification Using Pathological Images}, author = {Lu, Mengkang and Pan, Yongsheng and Nie, Dong and Liu, Feihong and Shi, Feng and Xia, Yong and Shen, Dinggang}, booktitle = {Proceedings of the MICCAI Workshop on Computational Pathology}, pages = {159--169}, year = {2021}, editor = {Atzori, Manfredo and Burlutskiy, Nikolay and Ciompi, Francesco and Li, Zhang and Minhas, Fayyaz and Müller, Henning and Peng, Tingying and Rajpoot, Nasir and Torben-Nielsen, Ben and van der Laak, Jeroen and Veta, Mitko and Yuan, Yinyin and Zlobec, Inti}, volume = {156}, series = {Proceedings of Machine Learning Research}, month = {27 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v156/lu21a/lu21a.pdf}, url = {https://proceedings.mlr.press/v156/lu21a.html}, abstract = {Gliomas are the most prevalent malignant brain tumor in adults and can be classified into four typical sub-types based on histological features. Histological diagnosis by pathologists via microscopic visual inspection of pathological slides has been the gold standard for glioma grading, especially hematoxylin and eosin (H&E) sections. However, due to spatial heterogeneity and complex tumor micro-environment, it is difficult and time-consuming for pathologists to differentiate glioma sub-types. In this paper, we propose a Sparse-attention based Multiple Instance contrastive LEarning (SMILE) method for glioma sub-type classification. First, we use contrastive learning to extract meaningful representations from pathological images. Second, we propose the sparse-attention multiple instance learning aggregator to get sparse instance representations in a bag for label prediction. We validate the proposed SMILE method using a glioma dataset from The Cancer Genome Atlas (TCGA). Experimental results show superior performance of our method over competing ones. Ablation study further demonstrates the effectiveness of our design of SMILE.} }
Endnote
%0 Conference Paper %T SMILE: Sparse-Attention based Multiple Instance Contrastive Learning for Glioma Sub-Type Classification Using Pathological Images %A Mengkang Lu %A Yongsheng Pan %A Dong Nie %A Feihong Liu %A Feng Shi %A Yong Xia %A Dinggang Shen %B Proceedings of the MICCAI Workshop on Computational Pathology %C Proceedings of Machine Learning Research %D 2021 %E Manfredo Atzori %E Nikolay Burlutskiy %E Francesco Ciompi %E Zhang Li %E Fayyaz Minhas %E Henning Müller %E Tingying Peng %E Nasir Rajpoot %E Ben Torben-Nielsen %E Jeroen van der Laak %E Mitko Veta %E Yinyin Yuan %E Inti Zlobec %F pmlr-v156-lu21a %I PMLR %P 159--169 %U https://proceedings.mlr.press/v156/lu21a.html %V 156 %X Gliomas are the most prevalent malignant brain tumor in adults and can be classified into four typical sub-types based on histological features. Histological diagnosis by pathologists via microscopic visual inspection of pathological slides has been the gold standard for glioma grading, especially hematoxylin and eosin (H&E) sections. However, due to spatial heterogeneity and complex tumor micro-environment, it is difficult and time-consuming for pathologists to differentiate glioma sub-types. In this paper, we propose a Sparse-attention based Multiple Instance contrastive LEarning (SMILE) method for glioma sub-type classification. First, we use contrastive learning to extract meaningful representations from pathological images. Second, we propose the sparse-attention multiple instance learning aggregator to get sparse instance representations in a bag for label prediction. We validate the proposed SMILE method using a glioma dataset from The Cancer Genome Atlas (TCGA). Experimental results show superior performance of our method over competing ones. Ablation study further demonstrates the effectiveness of our design of SMILE.
APA
Lu, M., Pan, Y., Nie, D., Liu, F., Shi, F., Xia, Y. & Shen, D.. (2021). SMILE: Sparse-Attention based Multiple Instance Contrastive Learning for Glioma Sub-Type Classification Using Pathological Images. Proceedings of the MICCAI Workshop on Computational Pathology, in Proceedings of Machine Learning Research 156:159-169 Available from https://proceedings.mlr.press/v156/lu21a.html.

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