100 research outputs found
Partial convolution based multimodal autoencoder for art investigation
Autoencoders have been widely used in applications with limited annotations to extract features in an unsupervised manner, pre-processing the data to be used in machine learning models. This is especially helpful in image processing for art investigation where annotated data is scarce and difficult to collect. We introduce a structural similarity index based loss function to train the autoencoder for image data. By extending the recently developed partial convolution to partial deconvolution, we construct a fully partial convolutional autoencoder (FP-CAE) and adapt it to multimodal data, typically utilized in art investigation. Experimental results on images of the Ghent Altarpieceshow that our method significantly suppresses edge artifacts and improves the overall reconstruction performance. The proposed FP-CAE can be used for data preprocessing in craquelure detection and other art investigation tasks in future studies
Unsupervised Deep Transfer Feature Learning for Medical Image Classification
The accuracy and robustness of image classification with supervised deep
learning are dependent on the availability of large-scale, annotated training
data. However, there is a paucity of annotated data available due to the
complexity of manual annotation. To overcome this problem, a popular approach
is to use transferable knowledge across different domains by: 1) using a
generic feature extractor that has been pre-trained on large-scale general
images (i.e., transfer-learned) but which not suited to capture characteristics
from medical images; or 2) fine-tuning generic knowledge with a relatively
smaller number of annotated images. Our aim is to reduce the reliance on
annotated training data by using a new hierarchical unsupervised feature
extractor with a convolutional auto-encoder placed atop of a pre-trained
convolutional neural network. Our approach constrains the rich and generic
image features from the pre-trained domain to a sophisticated representation of
the local image characteristics from the unannotated medical image domain. Our
approach has a higher classification accuracy than transfer-learned approaches
and is competitive with state-of-the-art supervised fine-tuned methods.Comment: 4 pages, 1 figure, 3 tables, Accepted (Oral) as IEEE International
Symposium on Biomedical Imaging 201
Relational Autoencoder for Feature Extraction
Feature extraction becomes increasingly important as data grows high
dimensional. Autoencoder as a neural network based feature extraction method
achieves great success in generating abstract features of high dimensional
data. However, it fails to consider the relationships of data samples which may
affect experimental results of using original and new features. In this paper,
we propose a Relation Autoencoder model considering both data features and
their relationships. We also extend it to work with other major autoencoder
models including Sparse Autoencoder, Denoising Autoencoder and Variational
Autoencoder. The proposed relational autoencoder models are evaluated on a set
of benchmark datasets and the experimental results show that considering data
relationships can generate more robust features which achieve lower
construction loss and then lower error rate in further classification compared
to the other variants of autoencoders.Comment: IJCNN-201
Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching
Automatic and reliable segmentation of the prostate is an important but difficult task for various clinical applications such as prostate cancer radiotherapy. The main challenges for accurate MR prostate localization lie in two aspects: (1) inhomogeneous and inconsistent appearance around prostate boundary, and (2) the large shape variation across different patients. To tackle these two problems, we propose a new deformable MR prostate segmentation method by unifying deep feature learning with the sparse patch matching. First, instead of directly using handcrafted features, we propose to learn the latent feature representation from prostate MR images by the stacked sparse auto-encoder (SSAE). Since the deep learning algorithm learns the feature hierarchy from the data, the learned features are often more concise and effective than the handcrafted features in describing the underlying data. To improve the discriminability of learned features, we further refine the feature representation in a supervised fashion. Second, based on the learned features, a sparse patch matching method is proposed to infer a prostate likelihood map by transferring the prostate labels from multiple atlases to the new prostate MR image. Finally, a deformable segmentation is used to integrate a sparse shape model with the prostate likelihood map for achieving the final segmentation. The proposed method has been extensively evaluated on the dataset that contains 66 T2-wighted prostate MR images. Experimental results show that the deep-learned features are more effective than the handcrafted features in guiding MR prostate segmentation. Moreover, our method shows superior performance than other state-of-the-art segmentation methods
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