Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Journal of pathology informatics ; h5-index 23.0

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.

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


Artificial intelligence, Breast cancer, Deep learning, Pathology