Journal Article

Weakly-supervised learning for lung carcinoma classification using deep learning

Iizuka, Osamu, Toyokawa, Gouji, Kozuma, Yuka, Momosaki, Seiya, Rambeau, Michael, Yamazaki, Koji, Takeo, Sadanori, Kanavati, Fahdi, Shoji, Fumihiro, Tsuneki, Masayuki

2020

Overview

Lung cancer is one of the major causes of cancer-related deaths in many countries around the world, and its histopathological diagnosis is crucial for deciding on optimum treatment strategies. Recently, Artificial Intelligence (AI) deep learning models have been widely shown to be useful in various medical fields, particularly image and pathological diagnoses; however, AI models for the pathological diagnosis of pulmonary lesions that have been validated on large-scale test sets are yet to be seen. We trained a Convolution Neural Network (CNN) based on the EfficientNet-B3 architecture, using transfer learning and weakly-supervised learning, to predict carcinoma in Whole Slide Images (WSIs) using a training dataset of 3,554 WSIs. We obtained highly promising results for differentiating between lung carcinoma and non-neoplastic with high Receiver Operator Curve (ROC) area under the curves (AUCs) on four independent test sets (ROC AUCs of 0.975, 0.974, 0.988, and 0.981, respectively). Development and validation of algorithms such as ours are important initial steps in the development of software suites that could be adopted in routine pathological practices and potentially help reduce the burden on pathologists.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 631/114/1305 / 631/67 / 631/67/1612 / Algorithms / Artificial Intelligence / Computational Biology - methods / Deep Learning / Diagnosis / Diagnosis, Computer-Assisted - methods / Humanities and Social Sciences / Humans / Lung cancer / Lung carcinoma / Lung Neoplasms - classification / Lung Neoplasms - diagnosis / Lung Neoplasms - pathology / multidisciplinary / Neural networks / Neural Networks, Computer / Pulmonary lesions / Science / Science (multidisciplinary) / Supervised Machine Learning / Transfer learning