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AuthorKashif, Muhammad Nasim
AuthorRaza, Shan E. Ahmed
AuthorSirinukunwattana, Korsuk
AuthorArif, Muhammmad
AuthorRajpoot, Nasir
Available date2021-09-01T10:03:27Z
Publication Date2016
Publication NameProceedings - International Symposium on Biomedical Imaging
ResourceScopus
URIhttp://dx.doi.org/10.1109/ISBI.2016.7493441
URIhttp://hdl.handle.net/10576/22449
AbstractDetection 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. 2016 IEEE.
Languageen
PublisherIEEE Computer Society
SubjectConvolution
Histology
Mathematical transformations
Medical imaging
Neural networks
Tumors
Convolutional neural network
Digital pathologies
Histology images
Invariant texture features
Nuclei detections
Pixel intensities
Scattering transforms
Texture characteristics
Feature extraction
TitleHandcrafted features with convolutional neural networks for detection of tumor cells in histology images
TypeConference Paper
Pagination1029-1032
Volume Number2016-June
dc.accessType Abstract Only


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