Deep Learning Features for Lung Adenocarcinoma Classification with Tissue Pathology Images

  • Jia He
  • Lin Shang
  • Hong Ji
  • XiuLing Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10637)


This paper presents the approach for lung adenocarcinoma diagnosis, using deep convolutional neural networks (CNN) to learn the features from the tissue pathology images. Our multi-stage procedure can detect the lung cancer of adenocarcinoma, in which the preprocessing consists of image enhancement and class imbalance treatment. Then Gradient-weighted Class Activation Mapping (Grad-CAM) and Guided-Backpropagation visualization techniques are employed to produce the visual explanations for decisions from our CNN model. Learned features and details for the specific areas have been generated through the model. Data is collected from 22 different patients with 270 lesion images and 24 normal ones. Experimental result on this data set has achieved F1-score with 0.963. Moreover, the study is not only to pursue precise classification on the tissue pathology images of lung adenocarcinoma, but also learn the specific areas in images which should be more concerned by doctors.


Deep learning Tissue pathology analysis Visualization 



This work is supported by the National Natural Science Foundation of China (No. 61672276) and Natural Science Foundation of Jiangsu, China (BK20161406).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.State Key Laboratory for Novel Software Technology, Department of Computer Science and TechnologyNanjing UniversityNanjingChina
  2. 2.Beijing Computing CenterBeijingChina

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