197 research outputs found
Machine learning approaches in medical image analysis: From detection to diagnosis
Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging protocols, learning from weak labels, and interpretation and evaluation of results
Why Does Synthesized Data Improve Multi-sequence Classification?
The classification and registration of incomplete multi-modal medical images, such as multi-sequence MRI with missing sequences, can sometimes be improved by replacing the missing modalities with synthetic data. This may seem counter-intuitive: synthetic data is derived from data that is already available, so it does not add new information. Why can it still improve performance? In this paper we discuss possible explanations. If the synthesis model is more flexible than the classifier, the synthesis model can provide features that the classifier could not have extracted from the original data. In addition, using synthetic information to complete incomplete samples increases the size of the training set.
We present experiments with two classifiers, linear support vector machines (SVMs) and random forests, together with two synthesis methods that can replace missing data in an image classification problem: neural networks and restricted Boltzmann machines (RBMs). We used data from the BRATS 2013 brain tumor segmentation challenge, which includes multi-modal MRI scans with T1, T1 post-contrast, T2 and FLAIR sequences. The linear SVMs appear to benefit from the complex transformations offered by the synthesis models, whereas the random forests mostly benefit from having more training data. Training on the hidden representation from the RBM brought the accuracy of the linear SVMs close to that of random forests
Transfer learning improves supervised image segmentation across imaging protocols
The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore may improve performance over supervised learning for segmentation across scanners and scan protocols. We present four transfer classifiers that can train a classification scheme with only a small amount of representative training data, in addition to a larger amount of other training data with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two magnetic resonance imaging brain-segmentation tasks with multi-site data: white matter, gray matter, and cerebrospinal fluid segmentation; and white-matter-/MS-lesion segmentation. The experiments showed that when there is only a small amount of representative training data available, transfer learning can greatly outperform common supervised-learning approaches, minimizing classification errors by up to 60%
Spectral Data Augmentation Techniques to quantify Lung Pathology from CT-images
Data augmentation is of paramount importance in biomedical image processing
tasks, characterized by inadequate amounts of labelled data, to best use all of
the data that is present. In-use techniques range from intensity
transformations and elastic deformations, to linearly combining existing data
points to make new ones. In this work, we propose the use of spectral
techniques for data augmentation, using the discrete cosine and wavelet
transforms. We empirically evaluate our approaches on a CT texture analysis
task to detect abnormal lung-tissue in patients with cystic fibrosis. Empirical
experiments show that the proposed spectral methods perform favourably as
compared to the existing methods. When used in combination with existing
methods, our proposed approach can increase the relative minor class
segmentation performance by 44.1% over a simple replication baseline.Comment: 5 pages including references, accepted as Oral presentation at IEEE
ISBI 202
Crowdsourcing Airway Annotations in Chest Computed Tomography Images
Measuring airways in chest computed tomography (CT) scans is important for
characterizing diseases such as cystic fibrosis, yet very time-consuming to
perform manually. Machine learning algorithms offer an alternative, but need
large sets of annotated scans for good performance. We investigate whether
crowdsourcing can be used to gather airway annotations. We generate image
slices at known locations of airways in 24 subjects and request the crowd
workers to outline the airway lumen and airway wall. After combining multiple
crowd workers, we compare the measurements to those made by the experts in the
original scans. Similar to our preliminary study, a large portion of the
annotations were excluded, possibly due to workers misunderstanding the
instructions. After excluding such annotations, moderate to strong correlations
with the expert can be observed, although these correlations are slightly lower
than inter-expert correlations. Furthermore, the results across subjects in
this study are quite variable. Although the crowd has potential in annotating
airways, further development is needed for it to be robust enough for gathering
annotations in practice. For reproducibility, data and code are available
online: \url{http://github.com/adriapr/crowdairway.git}
Hydranet: Data Augmentation for Regression Neural Networks
Deep learning techniques are often criticized to heavily depend on a large
quantity of labeled data. This problem is even more challenging in medical
image analysis where the annotator expertise is often scarce. We propose a
novel data-augmentation method to regularize neural network regressors that
learn from a single global label per image. The principle of the method is to
create new samples by recombining existing ones. We demonstrate the performance
of our algorithm on two tasks: estimation of the number of enlarged
perivascular spaces in the basal ganglia, and estimation of white matter
hyperintensities volume. We show that the proposed method improves the
performance over more basic data augmentation. The proposed method reached an
intraclass correlation coefficient between ground truth and network predictions
of 0.73 on the first task and 0.84 on the second task, only using between 25
and 30 scans with a single global label per scan for training. With the same
number of training scans, more conventional data augmentation methods could
only reach intraclass correlation coefficients of 0.68 on the first task, and
0.79 on the second task.Comment: accepted in MICCAI 201
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