4,165 research outputs found
MRI-based Surgical Planning for Lumbar Spinal Stenosis
The most common reason for spinal surgery in elderly patients is lumbar
spinal stenosis(LSS). For LSS, treatment decisions based on clinical and
radiological information as well as personal experience of the surgeon shows
large variance. Thus a standardized support system is of high value for a more
objective and reproducible decision. In this work, we develop an automated
algorithm to localize the stenosis causing the symptoms of the patient in
magnetic resonance imaging (MRI). With 22 MRI features of each of five spinal
levels of 321 patients, we show it is possible to predict the location of
lesion triggering the symptoms. To support this hypothesis, we conduct an
automated analysis of labeled and unlabeled MRI scans extracted from 788
patients. We confirm quantitatively the importance of radiological information
and provide an algorithmic pipeline for working with raw MRI scans
Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification
Implicit discourse relation classification is of great challenge due to the
lack of connectives as strong linguistic cues, which motivates the use of
annotated implicit connectives to improve the recognition. We propose a feature
imitation framework in which an implicit relation network is driven to learn
from another neural network with access to connectives, and thus encouraged to
extract similarly salient features for accurate classification. We develop an
adversarial model to enable an adaptive imitation scheme through competition
between the implicit network and a rival feature discriminator. Our method
effectively transfers discriminability of connectives to the implicit features,
and achieves state-of-the-art performance on the PDTB benchmark.Comment: To appear in ACL201
Combination of multiple neural networks using transfer learning and extensive geometric data augmentation for assessing cellularity scores in histopathology images
Classification of cancer cellularity within tissue samples is currently a
manual process performed by pathologists. This process of correctly determining
cancer cellularity can be time intensive. Deep Learning (DL) techniques in
particular have become increasingly more popular for this purpose, due to the
accuracy and performance they exhibit, which can be comparable to the
pathologists. This work investigates the capabilities of two DL approaches to
assess cancer cellularity in whole slide images (WSI) in the SPIE-AAPM-NCI
BreastPathQ challenge dataset. The effects of training on augmented data via
rotations, and combinations of multiple architectures into a single network
were analyzed using a modified Kendall Tau-b prediction probability metric
known as the average prediction probability PK. A deep, transfer learned,
Convolutional Neural Network (CNN) InceptionV3 was used as a baseline,
achieving an average PK value of 0.884, showing improvement from the average PK
value of 0.83 achieved by pathologists. The network was then trained on
additional training datasets which were rotated between 1 and 360 degrees,
which saw a peak increase of PK up to 4.2%. An additional architecture
consisting of the InceptionV3 network and VGG16, a shallow, transfer learned
CNN, was combined in a parallel architecture. This parallel architecture
achieved a baseline average PK value of 0.907, a statistically significantly
improvement over either of the architectures' performances separately (p<0.0001
by unpaired t-test).Comment: 7 pages (includes a cover page), 5 figures, 1 table, work addresses
the BreastPathQ challeng
DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation
Automatic organ segmentation is an important yet challenging problem for
medical image analysis. The pancreas is an abdominal organ with very high
anatomical variability. This inhibits previous segmentation methods from
achieving high accuracies, especially compared to other organs such as the
liver, heart or kidneys. In this paper, we present a probabilistic bottom-up
approach for pancreas segmentation in abdominal computed tomography (CT) scans,
using multi-level deep convolutional networks (ConvNets). We propose and
evaluate several variations of deep ConvNets in the context of hierarchical,
coarse-to-fine classification on image patches and regions, i.e. superpixels.
We first present a dense labeling of local image patches via
and nearest neighbor fusion. Then we describe a regional
ConvNet () that samples a set of bounding boxes around
each image superpixel at different scales of contexts in a "zoom-out" fashion.
Our ConvNets learn to assign class probabilities for each superpixel region of
being pancreas. Last, we study a stacked leveraging
the joint space of CT intensities and the dense
probability maps. Both 3D Gaussian smoothing and 2D conditional random fields
are exploited as structured predictions for post-processing. We evaluate on CT
images of 82 patients in 4-fold cross-validation. We achieve a Dice Similarity
Coefficient of 83.66.3% in training and 71.810.7% in testing.Comment: To be presented at MICCAI 2015 - 18th International Conference on
Medical Computing and Computer Assisted Interventions, Munich, German
Spectral-spatial classification of hyperspectral images: three tricks and a new supervised learning setting
Spectral-spatial classification of hyperspectral images has been the subject
of many studies in recent years. In the presence of only very few labeled
pixels, this task becomes challenging. In this paper we address the following
two research questions: 1) Can a simple neural network with just a single
hidden layer achieve state of the art performance in the presence of few
labeled pixels? 2) How is the performance of hyperspectral image classification
methods affected when using disjoint train and test sets? We give a positive
answer to the first question by using three tricks within a very basic shallow
Convolutional Neural Network (CNN) architecture: a tailored loss function, and
smooth- and label-based data augmentation. The tailored loss function enforces
that neighborhood wavelengths have similar contributions to the features
generated during training. A new label-based technique here proposed favors
selection of pixels in smaller classes, which is beneficial in the presence of
very few labeled pixels and skewed class distributions. To address the second
question, we introduce a new sampling procedure to generate disjoint train and
test set. Then the train set is used to obtain the CNN model, which is then
applied to pixels in the test set to estimate their labels. We assess the
efficacy of the simple neural network method on five publicly available
hyperspectral images. On these images our method significantly outperforms
considered baselines. Notably, with just 1% of labeled pixels per class, on
these datasets our method achieves an accuracy that goes from 86.42%
(challenging dataset) to 99.52% (easy dataset). Furthermore we show that the
simple neural network method improves over other baselines in the new
challenging supervised setting. Our analysis substantiates the highly
beneficial effect of using the entire image (so train and test data) for
constructing a model.Comment: Remote Sensing 201
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