21,813 research outputs found
Automatically Detecting Self-Reported Birth Defect Outcomes on Twitter for Large-scale Epidemiological Research
In recent work, we identified and studied a small cohort of Twitter users
whose pregnancies with birth defect outcomes could be observed via their
publicly available tweets. Exploiting social media's large-scale potential to
complement the limited methods for studying birth defects, the leading cause of
infant mortality, depends on the further development of automatic methods. The
primary objective of this study was to take the first step towards scaling the
use of social media for observing pregnancies with birth defect outcomes,
namely, developing methods for automatically detecting tweets by users
reporting their birth defect outcomes. We annotated and pre-processed
approximately 23,000 tweets that mention birth defects in order to train and
evaluate supervised machine learning algorithms, including feature-engineered
and deep learning-based classifiers. We also experimented with various
under-sampling and over-sampling approaches to address the class imbalance. A
Support Vector Machine (SVM) classifier trained on the original, imbalanced
data set, with n-grams, word clusters, and structural features, achieved the
best baseline performance for the positive classes: an F1-score of 0.65 for the
"defect" class and 0.51 for the "possible defect" class. Our contributions
include (i) natural language processing (NLP) and supervised machine learning
methods for automatically detecting tweets by users reporting their birth
defect outcomes, (ii) a comparison of feature-engineered and deep
learning-based classifiers trained on imbalanced, under-sampled, and
over-sampled data, and (iii) an error analysis that could inform classification
improvements using our publicly available corpus. Future work will focus on
automating user-level analyses for cohort inclusion
Anatomical Pattern Analysis for decoding visual stimuli in human brains
Background: A universal unanswered question in neuroscience and machine
learning is whether computers can decode the patterns of the human brain.
Multi-Voxels Pattern Analysis (MVPA) is a critical tool for addressing this
question. However, there are two challenges in the previous MVPA methods, which
include decreasing sparsity and noise in the extracted features and increasing
the performance of prediction.
Methods: In overcoming mentioned challenges, this paper proposes Anatomical
Pattern Analysis (APA) for decoding visual stimuli in the human brain. This
framework develops a novel anatomical feature extraction method and a new
imbalance AdaBoost algorithm for binary classification. Further, it utilizes an
Error-Correcting Output Codes (ECOC) method for multiclass prediction. APA can
automatically detect active regions for each category of the visual stimuli.
Moreover, it enables us to combine homogeneous datasets for applying advanced
classification.
Results and Conclusions: Experimental studies on 4 visual categories (words,
consonants, objects and scrambled photos) demonstrate that the proposed
approach achieves superior performance to state-of-the-art methods.Comment: Published in Cognitive Computatio
Machine Learning for Forecasting Mid Price Movement using Limit Order Book Data
Forecasting the movements of stock prices is one the most challenging
problems in financial markets analysis. In this paper, we use Machine Learning
(ML) algorithms for the prediction of future price movements using limit order
book data. Two different sets of features are combined and evaluated:
handcrafted features based on the raw order book data and features extracted by
ML algorithms, resulting in feature vectors with highly variant
dimensionalities. Three classifiers are evaluated using combinations of these
sets of features on two different evaluation setups and three prediction
scenarios. Even though the large scale and high frequency nature of the limit
order book poses several challenges, the scope of the conducted experiments and
the significance of the experimental results indicate that Machine Learning
highly befits this task carving the path towards future research in this field
Multi-level Feature Fusion-based CNN for Local Climate Zone Classification from Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset
As a unique classification scheme for urban forms and functions, the local
climate zone (LCZ) system provides essential general information for any
studies related to urban environments, especially on a large scale. Remote
sensing data-based classification approaches are the key to large-scale mapping
and monitoring of LCZs. The potential of deep learning-based approaches is not
yet fully explored, even though advanced convolutional neural networks (CNNs)
continue to push the frontiers for various computer vision tasks. One reason is
that published studies are based on different datasets, usually at a regional
scale, which makes it impossible to fairly and consistently compare the
potential of different CNNs for real-world scenarios. This study is based on
the big So2Sat LCZ42 benchmark dataset dedicated to LCZ classification. Using
this dataset, we studied a range of CNNs of varying sizes. In addition, we
proposed a CNN to classify LCZs from Sentinel-2 images, Sen2LCZ-Net. Using this
base network, we propose fusing multi-level features using the extended
Sen2LCZ-Net-MF. With this proposed simple network architecture and the highly
competitive benchmark dataset, we obtain results that are better than those
obtained by the state-of-the-art CNNs, while requiring less computation with
fewer layers and parameters. Large-scale LCZ classification examples of
completely unseen areas are presented, demonstrating the potential of our
proposed Sen2LCZ-Net-MF as well as the So2Sat LCZ42 dataset. We also
intensively investigated the influence of network depth and width and the
effectiveness of the design choices made for Sen2LCZ-Net-MF. Our work will
provide important baselines for future CNN-based algorithm developments for
both LCZ classification and other urban land cover land use classification
Scalable Deep Learning Logo Detection
Existing logo detection methods usually consider a small number of logo
classes and limited images per class with a strong assumption of requiring
tedious object bounding box annotations, therefore not scalable to real-world
dynamic applications. In this work, we tackle these challenges by exploring the
webly data learning principle without the need for exhaustive manual labelling.
Specifically, we propose a novel incremental learning approach, called Scalable
Logo Self-co-Learning (SL^2), capable of automatically self-discovering
informative training images from noisy web data for progressively improving
model capability in a cross-model co-learning manner. Moreover, we introduce a
very large (2,190,757 images of 194 logo classes) logo dataset "WebLogo-2M" by
an automatic web data collection and processing method. Extensive comparative
evaluations demonstrate the superiority of the proposed SL^2 method over the
state-of-the-art strongly and weakly supervised detection models and
contemporary webly data learning approaches
Separation of pulsar signals from noise with supervised machine learning algorithms
We evaluate the performance of four different machine learning (ML)
algorithms: an Artificial Neural Network Multi-Layer Perceptron (ANN MLP ),
Adaboost, Gradient Boosting Classifier (GBC), XGBoost, for the separation of
pulsars from radio frequency interference (RFI) and other sources of noise,
using a dataset obtained from the post-processing of a pulsar search pi peline.
This dataset was previously used for cross-validation of the SPINN-based
machine learning engine, used for the reprocessing of HTRU-S survey data
arXiv:1406.3627. We have used Synthetic Minority Over-sampling Technique
(SMOTE) to deal with high class imbalance in the dataset. We report a variety
of quality scores from all four of these algorithms on both the non-SMOTE and
SMOTE datasets. For all the above ML methods, we report high accuracy and
G-mean in both the non-SMOTE and SMOTE cases. We study the feature importances
using Adaboost, GBC, and XGBoost and also from the minimum Redundancy Maximum
Relevance approach to report algorithm-agnostic feature ranking. From these
methods, we find that the signal to noise of the folded profile to be the best
feature. We find that all the ML algorithms report FPRs about an order of
magnitude lower than the corresponding FPRs obtained in arXiv:1406.3627, for
the same recall value.Comment: 14 pages, 2 figures. Accepted for publication in Astronomy and
Computin
LapTool-Net: A Contextual Detector of Surgical Tools in Laparoscopic Videos Based on Recurrent Convolutional Neural Networks
We propose a new multilabel classifier, called LapTool-Net to detect the
presence of surgical tools in each frame of a laparoscopic video. The novelty
of LapTool-Net is the exploitation of the correlation among the usage of
different tools and, the tools and tasks - namely, the context of the tools'
usage. Towards this goal, the pattern in the co-occurrence of the tools is
utilized for designing a decision policy for a multilabel classifier based on a
Recurrent Convolutional Neural Network (RCNN) architecture to simultaneously
extract the spatio-temporal features. In contrast to the previous multilabel
classification methods, the RCNN and the decision model are trained in an
end-to-end manner using a multitask learning scheme. To overcome the high
imbalance and avoid overfitting caused by the lack of variety in the training
data, a high down-sampling rate is chosen based on the more frequent
combinations. Furthermore, at the post-processing step, the prediction for all
the frames of a video are corrected by designing a bi-directional RNN to model
the long-term task's order. LapTool-net was trained using a publicly available
dataset of laparoscopic cholecystectomy. The results show LapTool-Net
outperforms existing methods significantly, even while using fewer training
samples and a shallower architecture.Comment: 18 pages, 4 figures, Submitted to Medical Image Analysi
Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features
The presence of certain clinical dermoscopic features within a skin lesion
may indicate melanoma, and automatically detecting these features may lead to
more quantitative and reproducible diagnoses. We reformulate the task of
classifying clinical dermoscopic features within superpixels as a segmentation
problem, and propose a fully convolutional neural network to detect clinical
dermoscopic features from dermoscopy skin lesion images. Our neural network
architecture uses interpolated feature maps from several intermediate network
layers, and addresses imbalanced labels by minimizing a negative multi-label
Dice-F score, where the score is computed across the mini-batch for each
label. Our approach ranked first place in the 2017 ISIC-ISBI Part 2:
Dermoscopic Feature Classification Task challenge over both the provided
validation and test datasets, achieving a 0.895% area under the receiver
operator characteristic curve score. We show how simple baseline models can
outrank state-of-the-art approaches when using the official metrics of the
challenge, and propose to use a fuzzy Jaccard Index that ignores the empty set
(i.e., masks devoid of positive pixels) when ranking models. Our results
suggest that (i) the classification of clinical dermoscopic features can be
effectively approached as a segmentation problem, and (ii) the current metrics
used to rank models may not well capture the efficacy of the model. We plan to
make our trained model and code publicly available.Comment: Accepted JBHI versio
Surface Defect Classification in Real-Time Using Convolutional Neural Networks
Surface inspection systems are an important application domain for computer
vision, as they are used for defect detection and classification in the
manufacturing industry. Existing systems use hand-crafted features which
require extensive domain knowledge to create. Even though Convolutional neural
networks (CNNs) have proven successful in many large-scale challenges,
industrial inspection systems have yet barely realized their potential due to
two significant challenges: real-time processing speed requirements and
specialized narrow domain-specific datasets which are sometimes limited in
size. In this paper, we propose CNN models that are specifically designed to
handle capacity and real-time speed requirements of surface inspection systems.
To train and evaluate our network models, we created a surface image dataset
containing more than 22000 labeled images with many types of surface materials
and achieved 98.0% accuracy in binary defect classification. To solve the class
imbalance problem in our datasets, we introduce neural data augmentation
methods which are also applicable to similar domains that suffer from the same
problem. Our results show that deep learning based methods are feasible to be
used in surface inspection systems and outperform traditional methods in
accuracy and inference time by considerable margins.Comment: Supplementary material will follo
Feature versus Raw Sequence: Deep Learning Comparative Study on Predicting Pre-miRNA
Should we input known genome sequence features or input sequence itself in
deep learning framework? As deep learning more popular in various applications,
researchers often come to question whether to generate features or use raw
sequences for deep learning. To answer this question, we study the prediction
accuracy of precursor miRNA prediction of feature-based deep belief network and
sequence-based convolution neural network. Tested on a variant of six-layer
convolution neural net and three-layer deep belief network, we find the raw
sequence input based convolution neural network model performs similar or
slightly better than feature based deep belief networks with best accuracy
values of 0.995 and 0.990, respectively. Both the models outperform existing
benchmarks models. The results shows us that if provided large enough data,
well devised raw sequence based deep learning models can replace feature based
deep learning models. However, construction of well behaved deep learning model
can be very challenging. In cased features can be easily extracted,
feature-based deep learning models may be a better alternative.Comment: 12 pages, 2 figures. arXiv admin note: substantial text overlap with
arXiv:1704.0383
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