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Volume: 32 | Article ID: art00008
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Pathology image-based lung cancer subtyping using deeplearning features and cell-density maps
  DOI :  10.2352/ISSN.2470-1173.2020.10.IPAS-064  Published OnlineJanuary 2020
Abstract

In this paper, we propose a patch-based system to classify non-small cell lung cancer (NSCLC) diagnostic whole slide images (WSIs) into two major histopathological subtypes: adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC). Classifying patients accurately is important for prognosis and therapy decisions. The proposed system was trained and tested on 876 subtyped NSCLC gigapixel-resolution diagnostic WSIs from 805 patients – 664 in the training set and 141 in the test set. The algorithm has modules for: 1) auto-generated tumor/non-tumor masking using a trained residual neural network (ResNet34), 2) cell-density map generation (based on color deconvolution, local drain segmentation, and watershed transformation), 3) patch-level feature extraction using a pre-trained ResNet34, 4) a tower of linear SVMs for different cell ranges, and 5) a majority voting module for aggregating subtype predictions in unseen testing WSIs. The proposed system was trained and tested on several WSI magnifications ranging from x4 to x40 with a best ROC AUC of 0.95 and an accuracy of 0.86 in test samples. This fully-automated histopathology subtyping method outperforms similar published state-of-the-art methods for diagnostic WSIs.

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Mustafa I. Jaber, Christopher W. Szeto, Bing Song, Liudmila Beziaeva, Stephen C. Benz, Patrick Soon-Shiong, Shahrooz Rabizadeh, "Pathology image-based lung cancer subtyping using deeplearning features and cell-density mapsin Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Processing: Algorithms and Systems XVIII,  2020,  pp 64-1 - 64-5,  https://doi.org/10.2352/ISSN.2470-1173.2020.10.IPAS-064

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