184 research outputs found
Changes of Structure and Bonding with Thickness in Chalcogenide Thin Films
Extreme miniaturization is known to be detrimental for certain properties, such as ferroelectricity in perovskite oxide films below a critical thickness. Remarkably, few-layer crystalline films of monochalcogenides display robust in-plane ferroelectricity with potential applications in nanoelectronics. These applications critically depend on the electronic properties and the nature of bonding in the 2D limit. A fundamental open question is thus to what extent bulk properties persist in thin films. Here, this question is addressed by a first-principles study of the structural, electronic, and ferroelectric properties of selected monochalcogenides (GeSe, GeTe, SnSe, and SnTe) as a function of film thickness up to 18 bilayers. While in selenides a few bilayers are sufficient to recover the bulk behavior, the Te-based compounds deviate strongly from the bulk, irrespective of the slab thickness. These results are explained in terms of depolarizing fields in Te-based slabs and the different nature of the chemical bond in selenides and tellurides. It is shown that GeTe and SnTe slabs inherit metavalent bonding of the bulk phase, despite structural and electronic properties being strongly modified in thin films. This understanding of the nature of bonding in few-layers structures offers a powerful tool to tune materials properties for applications in information technology
Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease
We propose an automatic method using dilated convolutional neural networks
(CNNs) for segmentation of the myocardium and blood pool in cardiovascular MR
(CMR) of patients with congenital heart disease (CHD).
Ten training and ten test CMR scans cropped to an ROI around the heart were
provided in the MICCAI 2016 HVSMR challenge. A dilated CNN with a receptive
field of 131x131 voxels was trained for myocardium and blood pool segmentation
in axial, sagittal and coronal image slices. Performance was evaluated within
the HVSMR challenge.
Automatic segmentation of the test scans resulted in Dice indices of
0.800.06 and 0.930.02, average distances to boundaries of
0.960.31 and 0.890.24 mm, and Hausdorff distances of 6.133.76
and 7.073.01 mm for the myocardium and blood pool, respectively.
Segmentation took 41.514.7 s per scan.
In conclusion, dilated CNNs trained on a small set of CMR images of CHD
patients showing large anatomical variability provide accurate myocardium and
blood pool segmentations
Multi-Estimator Full Left Ventricle Quantification through Ensemble Learning
Cardiovascular disease accounts for 1 in every 4 deaths in United States.
Accurate estimation of structural and functional cardiac parameters is crucial
for both diagnosis and disease management. In this work, we develop an ensemble
learning framework for more accurate and robust left ventricle (LV)
quantification. The framework combines two 1st-level modules: direct estimation
module and a segmentation module. The direct estimation module utilizes
Convolutional Neural Network (CNN) to achieve end-to-end quantification. The
CNN is trained by taking 2D cardiac images as input and cardiac parameters as
output. The segmentation module utilizes a U-Net architecture for obtaining
pixel-wise prediction of the epicardium and endocardium of LV from the
background. The binary U-Net output is then analyzed by a separate CNN for
estimating the cardiac parameters. We then employ linear regression between the
1st-level predictor and ground truth to learn a 2nd-level predictor that
ensembles the results from 1st-level modules for the final estimation.
Preliminary results by testing the proposed framework on the LVQuan18 dataset
show superior performance of the ensemble learning model over the two base
modules.Comment: Jiasha Liu, Xiang Li and Hui Ren contribute equally to this wor
Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease
We propose a new iterative segmentation model which can be accurately learned
from a small dataset. A common approach is to train a model to directly segment
an image, requiring a large collection of manually annotated images to capture
the anatomical variability in a cohort. In contrast, we develop a segmentation
model that recursively evolves a segmentation in several steps, and implement
it as a recurrent neural network. We learn model parameters by optimizing the
interme- diate steps of the evolution in addition to the final segmentation. To
this end, we train our segmentation propagation model by presenting incom-
plete and/or inaccurate input segmentations paired with a recommended next
step. Our work aims to alleviate challenges in segmenting heart structures from
cardiac MRI for patients with congenital heart disease (CHD), which encompasses
a range of morphological deformations and topological changes. We demonstrate
the advantages of this approach on a dataset of 20 images from CHD patients,
learning a model that accurately segments individual heart chambers and great
vessels. Com- pared to direct segmentation, the iterative method yields more
accurate segmentation for patients with the most severe CHD malformations.Comment: Presented at the Deep Learning in Medical Image Analysis Workshop,
MICCAI 201
AutoSimulate: (Quickly) Learning Synthetic Data Generation
Simulation is increasingly being used for generating large labelled datasets
in many machine learning problems. Recent methods have focused on adjusting
simulator parameters with the goal of maximising accuracy on a validation task,
usually relying on REINFORCE-like gradient estimators. However these approaches
are very expensive as they treat the entire data generation, model training,
and validation pipeline as a black-box and require multiple costly objective
evaluations at each iteration. We propose an efficient alternative for optimal
synthetic data generation, based on a novel differentiable approximation of the
objective. This allows us to optimize the simulator, which may be
non-differentiable, requiring only one objective evaluation at each iteration
with a little overhead. We demonstrate on a state-of-the-art photorealistic
renderer that the proposed method finds the optimal data distribution faster
(up to ), with significantly reduced training data generation (up to
) and better accuracy () on real-world test datasets than
previous methods.Comment: ECCV 202
FSNet: An Identity-Aware Generative Model for Image-based Face Swapping
This paper presents FSNet, a deep generative model for image-based face
swapping. Traditionally, face-swapping methods are based on three-dimensional
morphable models (3DMMs), and facial textures are replaced between the
estimated three-dimensional (3D) geometries in two images of different
individuals. However, the estimation of 3D geometries along with different
lighting conditions using 3DMMs is still a difficult task. We herein represent
the face region with a latent variable that is assigned with the proposed deep
neural network (DNN) instead of facial textures. The proposed DNN synthesizes a
face-swapped image using the latent variable of the face region and another
image of the non-face region. The proposed method is not required to fit to the
3DMM; additionally, it performs face swapping only by feeding two face images
to the proposed network. Consequently, our DNN-based face swapping performs
better than previous approaches for challenging inputs with different face
orientations and lighting conditions. Through several experiments, we
demonstrated that the proposed method performs face swapping in a more stable
manner than the state-of-the-art method, and that its results are compatible
with the method thereof.Comment: 20pages, Asian Conference of Computer Vision 201
FU-net: Multi-class Image Segmentation Using Feedback Weighted U-net
In this paper, we present a generic deep convolutional neural network (DCNN)
for multi-class image segmentation. It is based on a well-established
supervised end-to-end DCNN model, known as U-net. U-net is firstly modified by
adding widely used batch normalization and residual block (named as BRU-net) to
improve the efficiency of model training. Based on BRU-net, we further
introduce a dynamically weighted cross-entropy loss function. The weighting
scheme is calculated based on the pixel-wise prediction accuracy during the
training process. Assigning higher weights to pixels with lower segmentation
accuracies enables the network to learn more from poorly predicted image
regions. Our method is named as feedback weighted U-net (FU-net). We have
evaluated our method based on T1- weighted brain MRI for the segmentation of
midbrain and substantia nigra, where the number of pixels in each class is
extremely unbalanced to each other. Based on the dice coefficient measurement,
our proposed FU-net has outperformed BRU-net and U-net with statistical
significance, especially when only a small number of training examples are
available. The code is publicly available in GitHub (GitHub link:
https://github.com/MinaJf/FU-net).Comment: Accepted for publication at International Conference on Image and
Graphics (ICIG 2019
Quantized Densely Connected U-Nets for Efficient Landmark Localization
In this paper, we propose quantized densely connected U-Nets for efficient
visual landmark localization. The idea is that features of the same semantic
meanings are globally reused across the stacked U-Nets. This dense connectivity
largely improves the information flow, yielding improved localization accuracy.
However, a vanilla dense design would suffer from critical efficiency issue in
both training and testing. To solve this problem, we first propose order-K
dense connectivity to trim off long-distance shortcuts; then, we use a
memory-efficient implementation to significantly boost the training efficiency
and investigate an iterative refinement that may slice the model size in half.
Finally, to reduce the memory consumption and high precision operations both in
training and testing, we further quantize weights, inputs, and gradients of our
localization network to low bit-width numbers. We validate our approach in two
tasks: human pose estimation and face alignment. The results show that our
approach achieves state-of-the-art localization accuracy, but using ~70% fewer
parameters, ~98% less model size and saving ~75% training memory compared with
other benchmark localizers. The code is available at
https://github.com/zhiqiangdon/CU-Net.Comment: ECCV201
Auto Segmentation of Lung in Non-small Cell Lung Cancer Using Deep Convolution Neural Network
Segmentation of Lung is the vital first step in radiologic diagnosis of lung cancer. In this work, we present a deep learning based automated technique that overcomes various shortcomings of traditional lung segmentation and explores the role of adding “explainability” to deep learning models so that the trust can be built on these models. Our approach shows better generalization across different scanner settings, vendors and the slice thickness. In addition, there is no initialization of the seed point making it complete automated without manual intervention. The dice score of 0.98 is achieved for lung segmentation on an independent data set of non-small cell lung cancer
Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice
The Dice score and Jaccard index are commonly used metrics for the evaluation
of segmentation tasks in medical imaging. Convolutional neural networks trained
for image segmentation tasks are usually optimized for (weighted)
cross-entropy. This introduces an adverse discrepancy between the learning
optimization objective (the loss) and the end target metric. Recent works in
computer vision have proposed soft surrogates to alleviate this discrepancy and
directly optimize the desired metric, either through relaxations (soft-Dice,
soft-Jaccard) or submodular optimization (Lov\'asz-softmax). The aim of this
study is two-fold. First, we investigate the theoretical differences in a risk
minimization framework and question the existence of a weighted cross-entropy
loss with weights theoretically optimized to surrogate Dice or Jaccard. Second,
we empirically investigate the behavior of the aforementioned loss functions
w.r.t. evaluation with Dice score and Jaccard index on five medical
segmentation tasks. Through the application of relative approximation bounds,
we show that all surrogates are equivalent up to a multiplicative factor, and
that no optimal weighting of cross-entropy exists to approximate Dice or
Jaccard measures. We validate these findings empirically and show that, while
it is important to opt for one of the target metric surrogates rather than a
cross-entropy-based loss, the choice of the surrogate does not make a
statistical difference on a wide range of medical segmentation tasks.Comment: MICCAI 201
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