Automatic breast carcinoma detection in histopathological micrographs based on Single Shot Multibox Detector

Mio Yamaguchi, Tomoaki Sasaki, Kodai Uemura, Yuichiro Tajima, Sho Kato, Kiyoshi Takagi, Yuto Yamazaki, Ryoko Saito-Koyama, Chihiro Inoue, Kurara Kawaguchi, Tomoya Soma, Toshio Miyata, Takashi Suzuki

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Background: A diagnosis with histological classification by pathologists is very important for appropriate treatments to improve the prognosis of patients with breast cancer. However, the number of pathologists is limited, and assisting the pathological diagnosis by artificial intelligence becomes very important. Here, we presented an automatic breast lesions detection model using microscopic histopathological images based on a Single Shot Multibox Detector (SSD) for the first time and evaluated its significance in assisting the diagnosis. Methods: We built the data set and trained the SSD model with 1361 microscopic images and evaluated using 315 images. Pathologists and medical students diagnosed the images with or without the assistance of the model to investigate the significance of our model in assisting the diagnosis. Results: The model achieved 88.3% and 90.5% diagnostic accuracies in 3-class (benign, non-invasive carcinoma, or invasive carcinoma) or 2-class (benign or malignant) classification tasks, respectively, and the mean intersection over union was 0.59. Medical students achieved a remarkably higher diagnostic accuracy score (average 84.7%) with the assistance of the model compared to those without assistance (average 67.4%). Some people diagnosed images in a short time using the assistance of the model (shorten by average 6.4 min) while others required a longer time (extended by 7.2 min). Conclusion: We presented the automatic breast lesions detection method at high speed using histopathological micrographs. The present system may conveniently support the histological diagnosis by pathologists in laboratories.

Original languageEnglish
Article number100147
JournalJournal of Pathology Informatics
Volume13
DOIs
Publication statusPublished - 2022 Jan

Keywords

  • Artificial intelligence
  • Breast cancer
  • Deep learning
  • Pathology

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