title: Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification.
abstract: Convolutional Neural Networks (CNN) are state-of-the-art models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible. The differentiation of cancer subtypes is based on cellular-level visual features observed on image patch scale. Therefore, we argue that in this situation, training a patch-level classifier on image patches will perform better than or similar to an image-level classifier. The challenge becomes how to intelligently combine patch-level classification results and model the fact that not all patches will be discriminative. We propose to train a decision fusion model to aggregate patch-level predictions given by patch-level CNNs, which to the best of our knowledge has not been shown before. Furthermore, we formulate a novel Expectation-Maximization (EM) based method that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches. We apply our method to the classification of glioma and non-small-cell lung carcinoma cases into subtypes. The classification accuracy of our method is similar to the inter-observer agreement between pathologists. Although it is impossible to train CNNs on WSIs, we experimentally demonstrate using a comparable non-cancer dataset of smaller images that a patch-based CNN can outperform an image-based CNN.
authors: Dept. of Computer Science, Stony Brook University.
authors: Le
authors: L
authors: Hou
authors: Dept. of Computer Science, Stony Brook University.
authors: Dimitris
authors: D
authors: Samaras
authors: Dept. of Biomedical Informatics, Stony Brook University; Oak Ridge National Laboratory.
authors: Tahsin M
authors: TM
authors: Kurc
authors: Dept. of Computer Science, Stony Brook University; Dept. of Biomedical Informatics, Stony Brook University; Dept. of Applied Mathematics and Statistics, Stony Brook University.
authors: Yi
authors: Y
authors: Gao
authors: Dept. of Pathology, Stony Brook Hospital.
authors: James E
authors: JE
authors: Davis
authors: Dept. of Computer Science, Stony Brook University; Dept. of Biomedical Informatics, Stony Brook University; Dept. of Pathology, Stony Brook Hospital; Cancer Center, Stony Brook Hospital.
authors: Joel H
authors: JH
authors: Saltz