Weakly Supervised Framework for Cancer Region Detection of Hepatocellular Carcinoma in Whole-Slide Pathologic Images Based on Multiscale Attention Convolutional Neural Network

Songhui Diao, Yinli Tian, Wanming Hu, Jiaxin Hou, Ricardo Lambo, Zhicheng Zhang, Yaoqin Xie, Xiu Nie, Fa Zhang, Daniel Racoceanu, Wenjian Qin

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

11 Citas (Scopus)

Resumen

Visual inspection of hepatocellular carcinoma cancer regions by experienced pathologists in whole-slide images (WSIs) is a challenging, labor-intensive, and time-consuming task because of the large scale and high resolution of WSIs. Therefore, a weakly supervised framework based on a multiscale attention convolutional neural network (MSAN-CNN) was introduced into this process. Herein, patch-based images with image-level normal/tumor annotation (rather than images with pixel-level annotation) were fed into a classification neural network. To further improve the performances of cancer region detection, multiscale attention was introduced into the classification neural network. A total of 100 cases were obtained from The Cancer Genome Atlas and divided into 70 training and 30 testing data sets that were fed into the MSAN-CNN framework. The experimental results showed that this framework significantly outperforms the single-scale detection method according to the area under the curve and accuracy, sensitivity, and specificity metrics. When compared with the diagnoses made by three pathologists, MSAN-CNN performed better than a junior- and an intermediate-level pathologist, and slightly worse than a senior pathologist. Furthermore, MSAN-CNN provided a very fast detection time compared with the pathologists. Therefore, a weakly supervised framework based on MSAN-CNN has great potential to assist pathologists in the fast and accurate detection of cancer regions of hepatocellular carcinoma on WSIs.

Idioma originalInglés
Páginas (desde-hasta)553-563
Número de páginas11
PublicaciónAmerican Journal of Pathology
Volumen192
N.º3
DOI
EstadoPublicada - mar. 2022
Publicado de forma externa

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