Deep Learning Features for Lung Adenocarcinoma Classification with Tissue Pathology Images

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.

[1]  Paul A. Viola,et al.  Multi-modal volume registration by maximization of mutual information , 1996, Medical Image Anal..

[2]  Max A. Viergever,et al.  2D image classification for 3D anatomy localization: employing deep convolutional neural networks , 2016, SPIE Medical Imaging.

[3]  Thomas Brox,et al.  Striving for Simplicity: The All Convolutional Net , 2014, ICLR.

[4]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[5]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[6]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[7]  Ghassan Hamarneh,et al.  Multi-resolution-Tract CNN with Hybrid Pretrained and Skin-Lesion Trained Layers , 2016, MLMI@MICCAI.

[8]  Dimitris N. Metaxas,et al.  Automated anatomical landmark detection ondistal femur surface using convolutional neural network , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[9]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Heung-Il Suk,et al.  Deep Learning in Medical Image Analysis. , 2017, Annual review of biomedical engineering.

[11]  Bolei Zhou,et al.  Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[13]  Ramprasaath R. Selvaraju,et al.  Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization , 2016 .

[14]  Wei Shen,et al.  Multi-scale Convolutional Neural Networks for Lung Nodule Classification , 2015, IPMI.

[15]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[16]  Karel J. Zuiderveld,et al.  Contrast Limited Adaptive Histogram Equalization , 1994, Graphics Gems.