Histopathological Image Classification Using Discriminative Feature-Oriented Dictionary Learning

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning cl...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2016-03, Vol.35 (3), p.738-751
Main Authors: Tiep Huu Vu, Mousavi, Hojjat Seyed, Monga, Vishal, Rao, Ganesh, Arvind Rao, U. K.
Format: Article
Language:English
Subjects:
Biomedical imaging
cancer grading
Classification
Dictionaries
dictionary learning
Feature extraction
Histocytochemistry - methods
Histopathological image analysis
Histopathological image classification
Humans
Image classification
Image Processing, Computer-Assisted - methods
Kidney - diagnostic imaging
Kidney - pathology
Learning
Lung - diagnostic imaging
Lung - pathology
Machine Learning
Neoplasms - diagnostic imaging
Neoplasms - pathology
Optimization
Proposals
sparse coding
Training
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Summary:In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2015.2493530