22,004 research outputs found
Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentation.
PurposeWith the advent of MR guided radiotherapy, internal organ motion can be imaged simultaneously during treatment. In this study, we evaluate the feasibility of pancreas MRI segmentation using state-of-the-art segmentation methods.Methods and materialT2 weighted HASTE and T1 weighted VIBE images were acquired on 3 patients and 2 healthy volunteers for a total of 12 imaging volumes. A novel dictionary learning (DL) method was used to segment the pancreas and compared to t mean-shift merging (MSM), distance regularized level set (DRLS), graph cuts (GC) and the segmentation results were compared to manual contours using Dice's index (DI), Hausdorff distance and shift of the-center-of-the-organ (SHIFT).ResultsAll VIBE images were successfully segmented by at least one of the auto-segmentation method with DI >0.83 and SHIFT ≤2 mm using the best automated segmentation method. The automated segmentation error of HASTE images was significantly greater. DL is statistically superior to the other methods in Dice's overlapping index. For the Hausdorff distance and SHIFT measurement, DRLS and DL performed slightly superior to the GC method, and substantially superior to MSM. DL required least human supervision and was faster to compute.ConclusionOur study demonstrated potential feasibility of automated segmentation of the pancreas on MRI images with minimal human supervision at the beginning of imaging acquisition. The achieved accuracy is promising for organ localization
ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering
We propose a novel attention based deep learning architecture for visual
question answering task (VQA). Given an image and an image related natural
language question, VQA generates the natural language answer for the question.
Generating the correct answers requires the model's attention to focus on the
regions corresponding to the question, because different questions inquire
about the attributes of different image regions. We introduce an attention
based configurable convolutional neural network (ABC-CNN) to learn such
question-guided attention. ABC-CNN determines an attention map for an
image-question pair by convolving the image feature map with configurable
convolutional kernels derived from the question's semantics. We evaluate the
ABC-CNN architecture on three benchmark VQA datasets: Toronto COCO-QA, DAQUAR,
and VQA dataset. ABC-CNN model achieves significant improvements over
state-of-the-art methods on these datasets. The question-guided attention
generated by ABC-CNN is also shown to reflect the regions that are highly
relevant to the questions
Modelling of Sound Events with Hidden Imbalances Based on Clustering and Separate Sub-Dictionary Learning
This paper proposes an effective modelling of sound event spectra with a
hidden data-size-imbalance, for improved Acoustic Event Detection (AED). The
proposed method models each event as an aggregated representation of a few
latent factors, while conventional approaches try to find acoustic elements
directly from the event spectra. In the method, all the latent factors across
all events are assigned comparable importance and complexity to overcome the
hidden imbalance of data-sizes in event spectra. To extract latent factors in
each event, the proposed method employs clustering and performs non-negative
matrix factorization to each latent factor, and learns its acoustic elements as
a sub-dictionary. Separate sub-dictionary learning effectively models the
acoustic elements with limited data-sizes and avoids over-fitting due to hidden
imbalances in training data. For the task of polyphonic sound event detection
from DCASE 2013 challenge, an AED based on the proposed modelling achieves a
detection F-measure of 46.5%, a significant improvement of more than 19% as
compared to the existing state-of-the-art methods
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