666 research outputs found
3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context
We present an efficient deep learning approach for the challenging task of
tumor segmentation in multisequence MR images. In recent years, Convolutional
Neural Networks (CNN) have achieved state-of-the-art performances in a large
variety of recognition tasks in medical imaging. Because of the considerable
computational cost of CNNs, large volumes such as MRI are typically processed
by subvolumes, for instance slices (axial, coronal, sagittal) or small 3D
patches. In this paper we introduce a CNN-based model which efficiently
combines the advantages of the short-range 3D context and the long-range 2D
context. To overcome the limitations of specific choices of neural network
architectures, we also propose to merge outputs of several cascaded 2D-3D
models by a voxelwise voting strategy. Furthermore, we propose a network
architecture in which the different MR sequences are processed by separate
subnetworks in order to be more robust to the problem of missing MR sequences.
Finally, a simple and efficient algorithm for training large CNN models is
introduced. We evaluate our method on the public benchmark of the BRATS 2017
challenge on the task of multiclass segmentation of malignant brain tumors. Our
method achieves good performances and produces accurate segmentations with
median Dice scores of 0.918 (whole tumor), 0.883 (tumor core) and 0.854
(enhancing core). Our approach can be naturally applied to various tasks
involving segmentation of lesions or organs.Comment: Submitted to the journal Computerized Medical Imaging and Graphic
Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification
Deep neural networks have led to state-of-the-art results in many medical imaging tasks including Alzheimerâs disease (AD) detection based on structural magnetic resonance imaging (MRI) data. However, the network decisions are often perceived as being highly non-transparent, making it difficult to apply these algorithms in clinical routine. In this study, we propose using layer-wise relevance propagation (LRP) to visualize convolutional neural network decisions for AD based on MRI data. Similarly to other visualization methods, LRP produces a heatmap in the input space indicating the importance/relevance of each voxel contributing to the final classification outcome. In contrast to susceptibility maps produced by guided backpropagation (âWhich change in voxels would change the outcome most?â), the LRP method is able to directly highlight positive contributions to the network classification in the input space. In particular, we show that (1) the LRP method is very specific for individuals (âWhy does this person have AD?â) with high inter-patient variability, (2) there is very little relevance for AD in healthy controls and (3) areas that exhibit a lot of relevance correlate well with what is known from literature. To quantify the latter, we compute size-corrected metrics of the summed relevance per brain area, e.g., relevance density or relevance gain. Although these metrics produce very individual âfingerprintsâ of relevance patterns for AD patients, a lot of importance is put on areas in the temporal lobe including the hippocampus. After discussing several limitations such as sensitivity toward the underlying model and computation parameters, we conclude that LRP might have a high potential to assist clinicians in explaining neural network decisions for diagnosing AD (and potentially other diseases) based on structural MRI data
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Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice.
Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today's world, robust detection of TBI has become more significant than ever. In this work, we investigate several machine learning approaches to assess their performance in classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms such as decision trees (DT), random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data from those of the control group in wake stages for different epoch lengths. Average power in different frequency sub-bands and alpha:theta power ratio in EEG were used as input features for machine learning approaches. Results in this mouse model were promising, suggesting similar approaches may be applicable to detect TBI in humans in practical scenarios
Robust sound event detection in bioacoustic sensor networks
Bioacoustic sensors, sometimes known as autonomous recording units (ARUs),
can record sounds of wildlife over long periods of time in scalable and
minimally invasive ways. Deriving per-species abundance estimates from these
sensors requires detection, classification, and quantification of animal
vocalizations as individual acoustic events. Yet, variability in ambient noise,
both over time and across sensors, hinders the reliability of current automated
systems for sound event detection (SED), such as convolutional neural networks
(CNN) in the time-frequency domain. In this article, we develop, benchmark, and
combine several machine listening techniques to improve the generalizability of
SED models across heterogeneous acoustic environments. As a case study, we
consider the problem of detecting avian flight calls from a ten-hour recording
of nocturnal bird migration, recorded by a network of six ARUs in the presence
of heterogeneous background noise. Starting from a CNN yielding
state-of-the-art accuracy on this task, we introduce two noise adaptation
techniques, respectively integrating short-term (60 milliseconds) and long-term
(30 minutes) context. First, we apply per-channel energy normalization (PCEN)
in the time-frequency domain, which applies short-term automatic gain control
to every subband in the mel-frequency spectrogram. Secondly, we replace the
last dense layer in the network by a context-adaptive neural network (CA-NN)
layer. Combining them yields state-of-the-art results that are unmatched by
artificial data augmentation alone. We release a pre-trained version of our
best performing system under the name of BirdVoxDetect, a ready-to-use detector
of avian flight calls in field recordings.Comment: 32 pages, in English. Submitted to PLOS ONE journal in February 2019;
revised August 2019; published October 201
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