Handcrafted features with convolutional neural networks for detection of tumor cells in histology images

Detection of tumor nuclei in cancer histology images requires sophisticated techniques due to the irregular shape, size and chromatin texture of the tumor nuclei. Some very recently proposed methods employ deep convolutional neural networks (CNNs) to detect cells in H&E stained images. However,...

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Bibliographic Details
Main Authors: Kashif, Muhammad Nasim, Ahmed Raza, Shan E., Sirinukunwattana, Korsuk, Arif, Muhammmad, Rajpoot, Nasir
Format: Conference Proceeding
Language:English
Subjects:
Convolutional Neural Network
Digital Pathology
Feature extraction
Histology
Image color analysis
Image detection
Neural networks
Nuclei
Raw
Scattering
Scattering Transform
Surface layer
Texture
Transforms
Tumor Nuclei Detection
Tumors
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Summary:Detection of tumor nuclei in cancer histology images requires sophisticated techniques due to the irregular shape, size and chromatin texture of the tumor nuclei. Some very recently proposed methods employ deep convolutional neural networks (CNNs) to detect cells in H&E stained images. However, all such methods use some form of raw pixel intensities as input and rely on the CNN to learn the deep features. In this work, we extend a recently proposed spatially constrained CNN (SC-CNN) by proposing features that capture texture characteristics and show that although CNN produces good results on automatically learned features, it can perform better if the input consists of a combination of handcrafted features and the raw data. The handcrafted features are computed through the scattering transform which gives non-linear invariant texture features. The combination of handcrafted features with raw data produces sharp proximity maps and better detection results than the results of raw intensities with a similar kind of CNN architecture.
ISSN:1945-8452
DOI:10.1109/ISBI.2016.7493441