Weakly supervised learning for classification of lung cytological images using attention-based multiple instance learning

Atsushi Teramoto, Yuka Kiriyama, Tetsuya Tsukamoto, Eiko Sakurai, Ayano Michiba, Kazuyoshi Imaizumi, Kuniaki Saito, Hiroshi Fujita

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

In cytological examination, suspicious cells are evaluated regarding malignancy and cancer type. To assist this, we previously proposed an automated method based on supervised learning that classifies cells in lung cytological images as benign or malignant. However, it is often difficult to label all cells. In this study, we developed a weakly supervised method for the classification of benign and malignant lung cells in cytological images using attention-based deep multiple instance learning (AD MIL). Images of lung cytological specimens were divided into small patch images and stored in bags. Each bag was then labeled as benign or malignant, and classification was conducted using AD MIL. The distribution of attention weights was also calculated as a color map to confirm the presence of malignant cells in the image. AD MIL using the AlexNet-like convolutional neural network model showed the best classification performance, with an accuracy of 0.916, which was better than that of supervised learning. In addition, an attention map of the entire image based on the attention weight allowed AD MIL to focus on most malignant cells. Our weakly supervised method automatically classifies cytological images with acceptable accuracy based on supervised learning without complex annotations.

Original languageEnglish
Article number20317
JournalScientific reports
Volume11
Issue number1
DOIs
Publication statusPublished - 12-2021

All Science Journal Classification (ASJC) codes

  • General

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