4,123 research outputs found
Detection and classification of masses in mammographic images in a multi-kernel approach
According to the World Health Organization, breast cancer is the main cause
of cancer death among adult women in the world. Although breast cancer occurs
indiscriminately in countries with several degrees of social and economic
development, among developing and underdevelopment countries mortality rates
are still high, due to low availability of early detection technologies. From
the clinical point of view, mammography is still the most effective diagnostic
technology, given the wide diffusion of the use and interpretation of these
images. Herein this work we propose a method to detect and classify
mammographic lesions using the regions of interest of images. Our proposal
consists in decomposing each image using multi-resolution wavelets. Zernike
moments are extracted from each wavelet component. Using this approach we can
combine both texture and shape features, which can be applied both to the
detection and classification of mammary lesions. We used 355 images of fatty
breast tissue of IRMA database, with 233 normal instances (no lesion), 72
benign, and 83 malignant cases. Classification was performed by using SVM and
ELM networks with modified kernels, in order to optimize accuracy rates,
reaching 94.11%. Considering both accuracy rates and training times, we defined
the ration between average percentage accuracy and average training time in a
reverse order. Our proposal was 50 times higher than the ratio obtained using
the best method of the state-of-the-art. As our proposed model can combine high
accuracy rate with low learning time, whenever a new data is received, our work
will be able to save a lot of time, hours, in learning process in relation to
the best method of the state-of-the-art
Discriminative Pattern Mining for Breast Cancer Histopathology Image Classification via Fully Convolutional Autoencoder
Accurate diagnosis of breast cancer in histopathology images is challenging
due to the heterogeneity of cancer cell growth as well as of a variety of
benign breast tissue proliferative lesions. In this paper, we propose a
practical and self-interpretable invasive cancer diagnosis solution. With
minimum annotation information, the proposed method mines contrast patterns
between normal and malignant images in unsupervised manner and generates a
probability map of abnormalities to verify its reasoning. Particularly, a fully
convolutional autoencoder is used to learn the dominant structural patterns
among normal image patches. Patches that do not share the characteristics of
this normal population are detected and analyzed by one-class support vector
machine and 1-layer neural network. We apply the proposed method to a public
breast cancer image set. Our results, in consultation with a senior
pathologist, demonstrate that the proposed method outperforms existing methods.
The obtained probability map could benefit the pathology practice by providing
visualized verification data and potentially leads to a better understanding of
data-driven diagnosis solutions
Deep Learning for identifying radiogenomic associations in breast cancer
Purpose: To determine whether deep learning models can distinguish between
breast cancer molecular subtypes based on dynamic contrast-enhanced magnetic
resonance imaging (DCE-MRI). Materials and methods: In this institutional
review board-approved single-center study, we analyzed DCE-MR images of 270
patients at our institution. Lesions of interest were identified by
radiologists. The task was to automatically determine whether the tumor is of
the Luminal A subtype or of another subtype based on the MR image patches
representing the tumor. Three different deep learning approaches were used to
classify the tumor according to their molecular subtypes: learning from scratch
where only tumor patches were used for training, transfer learning where
networks pre-trained on natural images were fine-tuned using tumor patches, and
off-the-shelf deep features where the features extracted by neural networks
trained on natural images were used for classification with a support vector
machine. Network architectures utilized in our experiments were GoogleNet, VGG,
and CIFAR. We used 10-fold crossvalidation method for validation and area under
the receiver operating characteristic (AUC) as the measure of performance.
Results: The best AUC performance for distinguishing molecular subtypes was
0.65 (95% CI:[0.57,0.71]) and was achieved by the off-the-shelf deep features
approach. The highest AUC performance for training from scratch was 0.58 (95%
CI:[0.51,0.64]) and the best AUC performance for transfer learning was 0.60
(95% CI:[0.52,0.65]) respectively. For the off-the-shelf approach, the features
extracted from the fully connected layer performed the best. Conclusion: Deep
learning may play a role in discovering radiogenomic associations in breast
cancer
Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ
Purpose: To determine whether deep learning-based algorithms applied to
breast MR images can aid in the prediction of occult invasive disease following
the di- agnosis of ductal carcinoma in situ (DCIS) by core needle biopsy.
Material and Methods: In this institutional review board-approved study, we
analyzed dynamic contrast-enhanced fat-saturated T1-weighted MRI sequences of
131 patients at our institution with a core needle biopsy-confirmed diagnosis
of DCIS. The patients had no preoperative therapy before breast MRI and no
prior history of breast cancer. We explored two different deep learning
approaches to predict whether there was a hidden (occult) invasive component in
the analyzed tumors that was ultimately detected at surgical excision. In the
first approach, we adopted the transfer learning strategy, in which a network
pre-trained on a large dataset of natural images is fine-tuned with our DCIS
images. Specifically, we used the GoogleNet model pre-trained on the ImageNet
dataset. In the second approach, we used a pre-trained network to extract deep
features, and a support vector machine (SVM) that utilizes these features to
predict the upstaging of the DCIS. We used 10-fold cross validation and the
area under the ROC curve (AUC) to estimate the performance of the predictive
models. Results: The best classification performance was obtained using the
deep features approach with GoogleNet model pre-trained on ImageNet as the
feature extractor and a polynomial kernel SVM used as the classifier (AUC =
0.70, 95% CI: 0.58- 0.79). For the transfer learning based approach, the
highest AUC obtained was 0.53 (95% CI: 0.41-0.62). Conclusion: Convolutional
neural networks could potentially be used to identify occult invasive disease
in patients diagnosed with DCIS at the initial core needle biopsy
Multispectral Spatial Characterization: Application to Mitosis Detection in Breast Cancer Histopathology
Accurate detection of mitosis plays a critical role in breast cancer
histopathology. Manual detection and counting of mitosis is tedious and subject
to considerable inter- and intra-reader variations. Multispectral imaging is a
recent medical imaging technology, proven successful in increasing the
segmentation accuracy in other fields. This study aims at improving the
accuracy of mitosis detection by developing a specific solution using
multispectral and multifocal imaging of breast cancer histopathological data.
We propose to enable clinical routine-compliant quality of mitosis
discrimination from other objects. The proposed framework includes
comprehensive analysis of spectral bands and z-stack focus planes, detection of
expected mitotic regions (candidates) in selected focus planes and spectral
bands, computation of multispectral spatial features for each candidate,
selection of multispectral spatial features and a study of different
state-of-the-art classification methods for candidates classification as
mitotic or non mitotic figures. This framework has been evaluated on MITOS
multispectral medical dataset and achieved 60% detection rate and 57%
F-Measure. Our results indicate that multispectral spatial features have more
information for mitosis classification in comparison with white spectral band
features, being therefore a very promising exploration area to improve the
quality of the diagnosis assistance in histopathology
TV News Commercials Detection using Success based Locally Weighted Kernel Combination
Commercial detection in news broadcast videos involves judicious selection of
meaningful audio-visual feature combinations and efficient classifiers. And,
this problem becomes much simpler if these combinations can be learned from the
data. To this end, we propose an Multiple Kernel Learning based method for
boosting successful kernel functions while ignoring the irrelevant ones. We
adopt a intermediate fusion approach where, a SVM is trained with a weighted
linear combination of different kernel functions instead of single kernel
function. Each kernel function is characterized by a feature set and kernel
type. We identify the feature sub-space locations of the prediction success of
a particular classifier trained only with particular kernel function. We
propose to estimate a weighing function using support vector regression (with
RBF kernel) for each kernel function which has high values (near 1.0) where the
classifier learned on kernel function succeeded and lower values (nearly 0.0)
otherwise. Second contribution of this work is TV News Commercials Dataset of
150 Hours of News videos. Classifier trained with our proposed scheme has
outperformed the baseline methods on 6 of 8 benchmark dataset and our own TV
commercials dataset
Receiver Operating Characteristic Curves and Confidence Bands for Support Vector Machines
Many problems that appear in biomedical decision making, such as diagnosing
disease and predicting response to treatment, can be expressed as binary
classification problems. The costs of false positives and false negatives vary
across application domains and receiver operating characteristic (ROC) curves
provide a visual representation of this trade-off. Nonparametric estimators for
the ROC curve, such as a weighted support vector machine (SVM), are desirable
because they are robust to model misspecification. While weighted SVMs have
great potential for estimating ROC curves, their theoretical properties were
heretofore underdeveloped. We propose a method for constructing confidence
bands for the SVM ROC curve and provide the theoretical justification for the
SVM ROC curve by showing that the risk function of the estimated decision rule
is uniformly consistent across the weight parameter. We demonstrate the
proposed confidence band method and the superior sensitivity and specificity of
the weighted SVM compared to commonly used methods in diagnostic medicine using
simulation studies. We present two illustrative examples: diagnosis of
hepatitis C and a predictive model for treatment response in breast cancer
On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset
This paper presents a comparison of six machine learning (ML) algorithms:
GRU-SVM (Agarap, 2017), Linear Regression, Multilayer Perceptron (MLP), Nearest
Neighbor (NN) search, Softmax Regression, and Support Vector Machine (SVM) on
the Wisconsin Diagnostic Breast Cancer (WDBC) dataset (Wolberg, Street, &
Mangasarian, 1992) by measuring their classification test accuracy and their
sensitivity and specificity values. The said dataset consists of features which
were computed from digitized images of FNA tests on a breast mass (Wolberg,
Street, & Mangasarian, 1992). For the implementation of the ML algorithms, the
dataset was partitioned in the following fashion: 70% for training phase, and
30% for the testing phase. The hyper-parameters used for all the classifiers
were manually assigned. Results show that all the presented ML algorithms
performed well (all exceeded 90% test accuracy) on the classification task. The
MLP algorithm stands out among the implemented algorithms with a test accuracy
of ~99.04%.Comment: 5 pages, 5 figures, 2 tables, presented at the International
Conference on Machine Learning and Soft Computing (ICMLSC) 2018 in Phu Quoc
Island, Viet Na
Incorporating Privileged Information to Unsupervised Anomaly Detection
We introduce a new unsupervised anomaly detection ensemble called SPI which
can harness privileged information - data available only for training examples
but not for (future) test examples. Our ideas build on the Learning Using
Privileged Information (LUPI) paradigm pioneered by Vapnik et al. [19,17],
which we extend to unsupervised learning and in particular to anomaly
detection. SPI (for Spotting anomalies with Privileged Information) constructs
a number of frames/fragments of knowledge (i.e., density estimates) in the
privileged space and transfers them to the anomaly scoring space through
"imitation" functions that use only the partial information available for test
examples. Our generalization of the LUPI paradigm to unsupervised anomaly
detection shepherds the field in several key directions, including (i) domain
knowledge-augmented detection using expert annotations as PI, (ii) fast
detection using computationally-demanding data as PI, and (iii) early detection
using "historical future" data as PI. Through extensive experiments on
simulated and real datasets, we show that augmenting privileged information to
anomaly detection significantly improves detection performance. We also
demonstrate the promise of SPI under all three settings (i-iii); with PI
capturing expert knowledge, computationally expensive features, and future data
on three real world detection tasks
Multi-Kernel LS-SVM Based Bio-Clinical Data Integration: Applications to Ovarian Cancer
The medical research facilitates to acquire a diverse type of data from the
same individual for particular cancer. Recent studies show that utilizing such
diverse data results in more accurate predictions. The major challenge faced is
how to utilize such diverse data sets in an effective way. In this paper, we
introduce a multiple kernel based pipeline for integrative analysis of
high-throughput molecular data (somatic mutation, copy number alteration, DNA
methylation and mRNA) and clinical data. We apply the pipeline on Ovarian
cancer data from TCGA. After multiple kernels have been generated from the
weighted sum of individual kernels, it is used to stratify patients and predict
clinical outcomes. We examine the survival time, vital status, and neoplasm
cancer status of each subtype to verify how well they cluster. We have also
examined the power of molecular and clinical data in predicting dichotomized
overall survival data and to classify the tumor grade for the cancer samples.
It was observed that the integration of various data types yields higher
log-rank statistics value. We were also able to predict clinical status with
higher accuracy as compared to using individual data types.Comment: 27 pages, 7 figures, extends the work presented in 6th International
Conference on Emerging Databases, accepted for publication in the IJDB
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