14,574 research outputs found
Lung Nodule Classification by the Combination of Fusion Classifier and Cascaded Convolutional Neural Networks
Lung nodule classification is a class imbalanced problem, as nodules are
found with much lower frequency than non-nodules. In the class imbalanced
problem, conventional classifiers tend to be overwhelmed by the majority class
and ignore the minority class. We showed that cascaded convolutional neural
networks can classify the nodule candidates precisely for a class imbalanced
nodule candidate data set in our previous study. In this paper, we propose
Fusion classifier in conjunction with the cascaded convolutional neural network
models. To fuse the models, nodule probabilities are calculated by using the
convolutional neural network models at first. Then, Fusion classifier is
trained and tested by the nodule probabilities. The proposed method achieved
the sensitivity of 94.4% and 95.9% at 4 and 8 false positives per scan in Free
Receiver Operating Characteristics (FROC) curve analysis, respectively.Comment: Draft of ISBI2018. arXiv admin note: text overlap with
arXiv:1703.0031
Deep learning for time series classification: a review
Time Series Classification (TSC) is an important and challenging problem in
data mining. With the increase of time series data availability, hundreds of
TSC algorithms have been proposed. Among these methods, only a few have
considered Deep Neural Networks (DNNs) to perform this task. This is surprising
as deep learning has seen very successful applications in the last years. DNNs
have indeed revolutionized the field of computer vision especially with the
advent of novel deeper architectures such as Residual and Convolutional Neural
Networks. Apart from images, sequential data such as text and audio can also be
processed with DNNs to reach state-of-the-art performance for document
classification and speech recognition. In this article, we study the current
state-of-the-art performance of deep learning algorithms for TSC by presenting
an empirical study of the most recent DNN architectures for TSC. We give an
overview of the most successful deep learning applications in various time
series domains under a unified taxonomy of DNNs for TSC. We also provide an
open source deep learning framework to the TSC community where we implemented
each of the compared approaches and evaluated them on a univariate TSC
benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By
training 8,730 deep learning models on 97 time series datasets, we propose the
most exhaustive study of DNNs for TSC to date.Comment: Accepted at Data Mining and Knowledge Discover
Abnormality Detection in Mammography using Deep Convolutional Neural Networks
Breast cancer is the most common cancer in women worldwide. The most common
screening technology is mammography. To reduce the cost and workload of
radiologists, we propose a computer aided detection approach for classifying
and localizing calcifications and masses in mammogram images. To improve on
conventional approaches, we apply deep convolutional neural networks (CNN) for
automatic feature learning and classifier building. In computer-aided
mammography, deep CNN classifiers cannot be trained directly on full mammogram
images because of the loss of image details from resizing at input layers.
Instead, our classifiers are trained on labelled image patches and then adapted
to work on full mammogram images for localizing the abnormalities.
State-of-the-art deep convolutional neural networks are compared on their
performance of classifying the abnormalities. Experimental results indicate
that VGGNet receives the best overall accuracy at 92.53\% in classifications.
For localizing abnormalities, ResNet is selected for computing class activation
maps because it is ready to be deployed without structural change or further
training. Our approach demonstrates that deep convolutional neural network
classifiers have remarkable localization capabilities despite no supervision on
the location of abnormalities is provided.Comment: 6 page
Short-segment heart sound classification using an ensemble of deep convolutional neural networks
This paper proposes a framework based on deep convolutional neural networks
(CNNs) for automatic heart sound classification using short-segments of
individual heart beats. We design a 1D-CNN that directly learns features from
raw heart-sound signals, and a 2D-CNN that takes inputs of two- dimensional
time-frequency feature maps based on Mel-frequency cepstral coefficients
(MFCC). We further develop a time-frequency CNN ensemble (TF-ECNN) combining
the 1D-CNN and 2D-CNN based on score-level fusion of the class probabilities.
On the large PhysioNet CinC challenge 2016 database, the proposed CNN models
outperformed traditional classifiers based on support vector machine and hidden
Markov models with various hand-crafted time- and frequency-domain features.
Best classification scores with 89.22% accuracy and 89.94% sensitivity were
achieved by the ECNN, and 91.55% specificity and 88.82% modified accuracy by
the 2D-CNN alone on the test set.Comment: 8 pages, 1 figure, conferenc
Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks
It is desirable to train convolutional networks (CNNs) to run more
efficiently during inference. In many cases however, the computational budget
that the system has for inference cannot be known beforehand during training,
or the inference budget is dependent on the changing real-time resource
availability. Thus, it is inadequate to train just inference-efficient CNNs,
whose inference costs are not adjustable and cannot adapt to varied inference
budgets. We propose a novel approach for cost-adjustable inference in CNNs -
Stochastic Downsampling Point (SDPoint). During training, SDPoint applies
feature map downsampling to a random point in the layer hierarchy, with a
random downsampling ratio. The different stochastic downsampling configurations
known as SDPoint instances (of the same model) have computational costs
different from each other, while being trained to minimize the same prediction
loss. Sharing network parameters across different instances provides
significant regularization boost. During inference, one may handpick a SDPoint
instance that best fits the inference budget. The effectiveness of SDPoint, as
both a cost-adjustable inference approach and a regularizer, is validated
through extensive experiments on image classification
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