116 research outputs found
Discovery Radiomics via Deep Multi-Column Radiomic Sequencers for Skin Cancer Detection
While skin cancer is the most diagnosed form of cancer in men and women, with
more cases diagnosed each year than all other cancers combined, sufficiently
early diagnosis results in very good prognosis and as such makes early
detection crucial. While radiomics have shown considerable promise as a
powerful diagnostic tool for significantly improving oncological diagnostic
accuracy and efficiency, current radiomics-driven methods have largely rely on
pre-defined, hand-crafted quantitative features, which can greatly limit the
ability to fully characterize unique cancer phenotype that distinguish it from
healthy tissue. Recently, the notion of discovery radiomics was introduced,
where a large amount of custom, quantitative radiomic features are directly
discovered from the wealth of readily available medical imaging data. In this
study, we present a novel discovery radiomics framework for skin cancer
detection, where we leverage novel deep multi-column radiomic sequencers for
high-throughput discovery and extraction of a large amount of custom radiomic
features tailored for characterizing unique skin cancer tissue phenotype. The
discovered radiomic sequencer was tested against 9,152 biopsy-proven clinical
images comprising of different skin cancers such as melanoma and basal cell
carcinoma, and demonstrated sensitivity and specificity of 91% and 75%,
respectively, thus achieving dermatologist-level performance and \break hence
can be a powerful tool for assisting general practitioners and dermatologists
alike in improving the efficiency, consistency, and accuracy of skin cancer
diagnosis
Efficient Deep Feature Learning and Extraction via StochasticNets
Deep neural networks are a powerful tool for feature learning and extraction
given their ability to model high-level abstractions in highly complex data.
One area worth exploring in feature learning and extraction using deep neural
networks is efficient neural connectivity formation for faster feature learning
and extraction. Motivated by findings of stochastic synaptic connectivity
formation in the brain as well as the brain's uncanny ability to efficiently
represent information, we propose the efficient learning and extraction of
features via StochasticNets, where sparsely-connected deep neural networks can
be formed via stochastic connectivity between neurons. To evaluate the
feasibility of such a deep neural network architecture for feature learning and
extraction, we train deep convolutional StochasticNets to learn abstract
features using the CIFAR-10 dataset, and extract the learned features from
images to perform classification on the SVHN and STL-10 datasets. Experimental
results show that features learned using deep convolutional StochasticNets,
with fewer neural connections than conventional deep convolutional neural
networks, can allow for better or comparable classification accuracy than
conventional deep neural networks: relative test error decrease of ~4.5% for
classification on the STL-10 dataset and ~1% for classification on the SVHN
dataset. Furthermore, it was shown that the deep features extracted using deep
convolutional StochasticNets can provide comparable classification accuracy
even when only 10% of the training data is used for feature learning. Finally,
it was also shown that significant gains in feature extraction speed can be
achieved in embedded applications using StochasticNets. As such, StochasticNets
allow for faster feature learning and extraction performance while facilitate
for better or comparable accuracy performances.Comment: 10 pages. arXiv admin note: substantial text overlap with
arXiv:1508.0546
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