Dual Structured Convolutional Neural Network with Feature Augmentation for Quantitative Characterization of Tissue Histology
Autor: | Kaisa Liimatainen, Matti Nykter, Leena Latonen, Pekka Ruusuvuori, Mira Valkonen, Kimmo Kartasalo |
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Rok vydání: | 2017 |
Předmět: |
0301 basic medicine
Elastic net regularization Computer science business.industry Feature vector Feature extraction Supervised learning Digital pathology Pattern recognition Convolutional neural network 030218 nuclear medicine & medical imaging 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Discriminative model Feature (computer vision) Histogram Artificial intelligence business |
Zdroj: | ICCV Workshops |
Popis: | We present a dual convolutional neural network (dCNN) architecture for extracting multi-scale features from histological tissue images for the purpose of automated characterization of tissue in digital pathology. The dual structure consists of two identical convolutional neural networks applied to input images with different scales, that are merged together and stacked with two fully connected layers. It has been acknowledged that deep networks can be used to extract higher-order features, and therefore, the network output at final fully connected layer was used as a deep dCNN feature vector. Further, engineered features, shown in previous studies to capture important characteristics of tissue structure and morphology, were integrated to the feature extractor module. The acquired quantitative feature representation can be further utilized to train a discriminative model for classifying tissue types. Machine learning based methods for detection of regions of interest, or tissue type classification will advance the transition to decision support systems and computer aided diagnosis in digital pathology. Here we apply the proposed feature-augmented dCNN method with supervised learning in detecting cancerous tissue from whole slide images. The extracted quantitative representation of tissue histology was used to train a logistic regression model with elastic net regularization. The model was able to accurately discriminate cancerous tissue from normal tissue, resulting in blockwise AUC=0.97, where the total number of analyzed tissue blocks was approximately 8.3 million that constitute the test set of 75 whole slide images. |
Databáze: | OpenAIRE |
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