TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Dual Structured Convolutional Neural Network with Feature Augmentation for Quantitative Characterization of Tissue Histology

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
KustantajaIEEE
Sivut27-35
Sivumäärä9
ISBN (elektroninen)9781538610343
DOI - pysyväislinkit
TilaJulkaistu - 19 tammikuuta 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Conference on Computer Vision Workshops -
Kesto: 1 tammikuuta 1900 → …

Conference

ConferenceIEEE International Conference on Computer Vision Workshops
Ajanjakso1/01/00 → …

Tiivistelmä

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.