A Self-Adaptive Discriminative Autoencoder for Medical Applications

Computer aided diagnosis (CAD) systems play an essential role in the early detection and diagnosis of developing disease for medical applications. In order to obtain the highly recognizable representation for the medical images, a self-adaptive discriminative autoencoder (SADAE) is proposed in this...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology Vol. 32; no. 12; pp. 8875 - 8886
Main Authors: Ge, Xiaolong, Qu, Yanpeng, Shang, Changjing, Yang, Longzhi, Shen, Qiang
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Autoencoder network
computer aided diagnosis
Data mining
Deep learning
Diagnosis
Face recognition
Feature extraction
Feature recognition
Image quality
Measurement
Medical diagnostic imaging
Medical imaging
metric learning
Object recognition
Representations
Subspaces
Teaching methods
ISSN:1051-8215, 1558-2205
Online Access:Get full text
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Summary:Computer aided diagnosis (CAD) systems play an essential role in the early detection and diagnosis of developing disease for medical applications. In order to obtain the highly recognizable representation for the medical images, a self-adaptive discriminative autoencoder (SADAE) is proposed in this paper. The proposed SADAE system is implemented under a deep metric learning framework which consists of <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula> local autoencoders, employed to learn the <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula> subspaces that represent the diverse distribution of the underlying data, and a global autoencoder to restrict the spatial scale of the learned representation of images. Such community of autoencoders is aided by a self-adaptive metric learning method that extracts the discriminative features to recognize the different categories in the given images. The quality of the extracted features by SADAE is compared against that of those extracted by other state-of-the-art deep learning and metric learning methods on five popular medical image data sets. The experimental results demonstrate that the medical image recognition results gained by SADAE are much improved over those by the alternatives.
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ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2022.3195727