Fusing Heterogeneous Features From Stacked Sparse Autoencoder for Histopathological Image Analysis

In the analysis of histopathological images, both holistic (e.g., architecture features) and local appearance features demonstrate excellent performance, while their accuracy may vary dramatically when providing different inputs. This motivates us to investigate how to fuse results from these featur...

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Veröffentlicht in:IEEE journal of biomedical and health informatics Jg. 20; H. 5; S. 1377 - 1383
Hauptverfasser: Zhang, Xiaofan, Dou, Hang, Ju, Tao, Xu, Jun, Zhang, Shaoting
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.09.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Accuracy
Algorithms
Biomedical imaging
Breast
Breast - diagnostic imaging
Breast lesion
Breast Neoplasms - diagnostic imaging
Cell Nucleus - pathology
Classification
Computer architecture
Diagnosis
Feature extraction
feature fusion
Female
Fuses
Histocytochemistry - methods
histopathological image analysis
Humans
Image analysis
Image classification
Image detection
Image Interpretation, Computer-Assisted - methods
Image retrieval
large-scale image retrieval
Machine Learning
Representations
Retrieval
stacked sparse autoencoder (SSAE)
ISSN:2168-2194, 2168-2208, 2168-2208
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Zusammenfassung:In the analysis of histopathological images, both holistic (e.g., architecture features) and local appearance features demonstrate excellent performance, while their accuracy may vary dramatically when providing different inputs. This motivates us to investigate how to fuse results from these features to enhance the accuracy. Particularly, we employ content-based image retrieval approaches to discover morphologically relevant images for image-guided diagnosis, using holistic and local features, both of which are generated from the cell detection results by a stacked sparse autoencoder. Because of the dramatically different characteristics and representations of these heterogeneous features (i.e., holistic and local), their results may not agree with each other, causing difficulties for traditional fusion methods. In this paper, we employ a graph-based query-specific fusion approach where multiple retrieval results (i.e., rank lists) are integrated and reordered based on a fused graph. The proposed method is capable of combining the strengths of local or holistic features adaptively for different inputs. We evaluate our method on a challenging clinical problem, i.e., histopathological image-guided diagnosis of intraductal breast lesions, and it achieves 91.67% classification accuracy on 120 breast tissue images from 40 patients.
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2015.2461671