8,303 research outputs found
Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ
Purpose: To determine whether deep learning-based algorithms applied to
breast MR images can aid in the prediction of occult invasive disease following
the di- agnosis of ductal carcinoma in situ (DCIS) by core needle biopsy.
Material and Methods: In this institutional review board-approved study, we
analyzed dynamic contrast-enhanced fat-saturated T1-weighted MRI sequences of
131 patients at our institution with a core needle biopsy-confirmed diagnosis
of DCIS. The patients had no preoperative therapy before breast MRI and no
prior history of breast cancer. We explored two different deep learning
approaches to predict whether there was a hidden (occult) invasive component in
the analyzed tumors that was ultimately detected at surgical excision. In the
first approach, we adopted the transfer learning strategy, in which a network
pre-trained on a large dataset of natural images is fine-tuned with our DCIS
images. Specifically, we used the GoogleNet model pre-trained on the ImageNet
dataset. In the second approach, we used a pre-trained network to extract deep
features, and a support vector machine (SVM) that utilizes these features to
predict the upstaging of the DCIS. We used 10-fold cross validation and the
area under the ROC curve (AUC) to estimate the performance of the predictive
models. Results: The best classification performance was obtained using the
deep features approach with GoogleNet model pre-trained on ImageNet as the
feature extractor and a polynomial kernel SVM used as the classifier (AUC =
0.70, 95% CI: 0.58- 0.79). For the transfer learning based approach, the
highest AUC obtained was 0.53 (95% CI: 0.41-0.62). Conclusion: Convolutional
neural networks could potentially be used to identify occult invasive disease
in patients diagnosed with DCIS at the initial core needle biopsy
Deep Learning for identifying radiogenomic associations in breast cancer
Purpose: To determine whether deep learning models can distinguish between
breast cancer molecular subtypes based on dynamic contrast-enhanced magnetic
resonance imaging (DCE-MRI). Materials and methods: In this institutional
review board-approved single-center study, we analyzed DCE-MR images of 270
patients at our institution. Lesions of interest were identified by
radiologists. The task was to automatically determine whether the tumor is of
the Luminal A subtype or of another subtype based on the MR image patches
representing the tumor. Three different deep learning approaches were used to
classify the tumor according to their molecular subtypes: learning from scratch
where only tumor patches were used for training, transfer learning where
networks pre-trained on natural images were fine-tuned using tumor patches, and
off-the-shelf deep features where the features extracted by neural networks
trained on natural images were used for classification with a support vector
machine. Network architectures utilized in our experiments were GoogleNet, VGG,
and CIFAR. We used 10-fold crossvalidation method for validation and area under
the receiver operating characteristic (AUC) as the measure of performance.
Results: The best AUC performance for distinguishing molecular subtypes was
0.65 (95% CI:[0.57,0.71]) and was achieved by the off-the-shelf deep features
approach. The highest AUC performance for training from scratch was 0.58 (95%
CI:[0.51,0.64]) and the best AUC performance for transfer learning was 0.60
(95% CI:[0.52,0.65]) respectively. For the off-the-shelf approach, the features
extracted from the fully connected layer performed the best. Conclusion: Deep
learning may play a role in discovering radiogenomic associations in breast
cancer
Med3D: Transfer Learning for 3D Medical Image Analysis
The performance on deep learning is significantly affected by volume of
training data. Models pre-trained from massive dataset such as ImageNet become
a powerful weapon for speeding up training convergence and improving accuracy.
Similarly, models based on large dataset are important for the development of
deep learning in 3D medical images. However, it is extremely challenging to
build a sufficiently large dataset due to difficulty of data acquisition and
annotation in 3D medical imaging. We aggregate the dataset from several medical
challenges to build 3DSeg-8 dataset with diverse modalities, target organs, and
pathologies. To extract general medical three-dimension (3D) features, we
design a heterogeneous 3D network called Med3D to co-train multi-domain 3DSeg-8
so as to make a series of pre-trained models. We transfer Med3D pre-trained
models to lung segmentation in LIDC dataset, pulmonary nodule classification in
LIDC dataset and liver segmentation on LiTS challenge. Experiments show that
the Med3D can accelerate the training convergence speed of target 3D medical
tasks 2 times compared with model pre-trained on Kinetics dataset, and 10 times
compared with training from scratch as well as improve accuracy ranging from 3%
to 20%. Transferring our Med3D model on state-the-of-art DenseASPP segmentation
network, in case of single model, we achieve 94.6\% Dice coefficient which
approaches the result of top-ranged algorithms on the LiTS challenge
Distilling with Performance Enhanced Students
The task of accelerating large neural networks on general purpose hardware
has, in recent years, prompted the use of channel pruning to reduce network
size. However, the efficacy of pruning based approaches has since been called
into question. In this paper, we turn to distillation for model
compression---specifically, attention transfer---and develop a simple method
for discovering performance enhanced student networks. We combine channel
saliency metrics with empirical observations of runtime performance to design
more accurate networks for a given latency budget. We apply our methodology to
residual and densely-connected networks, and show that we are able to find
resource-efficient student networks on different hardware platforms while
maintaining very high accuracy. These performance-enhanced student networks
achieve up to 10% boosts in top-1 ImageNet accuracy over their channel-pruned
counterparts for the same inference time.Comment: Preprint. Paper title has change
Deep Radiomics for Brain Tumor Detection and Classification from Multi-Sequence MRI
Glioma constitutes 80% of malignant primary brain tumors and is usually
classified as HGG and LGG. The LGG tumors are less aggressive, with slower
growth rate as compared to HGG, and are responsive to therapy. Tumor biopsy
being challenging for brain tumor patients, noninvasive imaging techniques like
Magnetic Resonance Imaging (MRI) have been extensively employed in diagnosing
brain tumors. Therefore automated systems for the detection and prediction of
the grade of tumors based on MRI data becomes necessary for assisting doctors
in the framework of augmented intelligence. In this paper, we thoroughly
investigate the power of Deep ConvNets for classification of brain tumors using
multi-sequence MR images. We propose novel ConvNet models, which are trained
from scratch, on MRI patches, slices, and multi-planar volumetric slices. The
suitability of transfer learning for the task is next studied by applying two
existing ConvNets models (VGGNet and ResNet) trained on ImageNet dataset,
through fine-tuning of the last few layers. LOPO testing, and testing on the
holdout dataset are used to evaluate the performance of the ConvNets. Results
demonstrate that the proposed ConvNets achieve better accuracy in all cases
where the model is trained on the multi-planar volumetric dataset. Unlike
conventional models, it obtains a testing accuracy of 95% for the low/high
grade glioma classification problem. A score of 97% is generated for
classification of LGG with/without 1p/19q codeletion, without any additional
effort towards extraction and selection of features. We study the properties of
self-learned kernels/ filters in different layers, through visualization of the
intermediate layer outputs. We also compare the results with that of
state-of-the-art methods, demonstrating a maximum improvement of 7% on the
grading performance of ConvNets and 9% on the prediction of 1p/19q codeletion
status
Representation Learning on Large and Small Data
Deep learning owes its success to three key factors: scale of data, enhanced
models to learn representations from data, and scale of computation. This book
chapter presented the importance of the data-driven approach to learn good
representations from both big data and small data. In terms of big data, it has
been widely accepted in the research community that the more data the better
for both representation and classification improvement. The question is then
how to learn representations from big data, and how to perform representation
learning when data is scarce. We addressed the first question by presenting CNN
model enhancements in the aspects of representation, optimization, and
generalization. To address the small data challenge, we showed transfer
representation learning to be effective. Transfer representation learning
transfers the learned representation from a source domain where abundant
training data is available to a target domain where training data is scarce.
Transfer representation learning gave the OM and melanoma diagnosis modules of
our XPRIZE Tricorder device (which finished out of competing
teams) a significant boost in diagnosis accuracy.Comment: Book chapte
Deep Aesthetic Quality Assessment with Semantic Information
Human beings often assess the aesthetic quality of an image coupled with the
identification of the image's semantic content. This paper addresses the
correlation issue between automatic aesthetic quality assessment and semantic
recognition. We cast the assessment problem as the main task among a multi-task
deep model, and argue that semantic recognition task offers the key to address
this problem. Based on convolutional neural networks, we employ a single and
simple multi-task framework to efficiently utilize the supervision of aesthetic
and semantic labels. A correlation item between these two tasks is further
introduced to the framework by incorporating the inter-task relationship
learning. This item not only provides some useful insight about the correlation
but also improves assessment accuracy of the aesthetic task. Particularly, an
effective strategy is developed to keep a balance between the two tasks, which
facilitates to optimize the parameters of the framework. Extensive experiments
on the challenging AVA dataset and Photo.net dataset validate the importance of
semantic recognition in aesthetic quality assessment, and demonstrate that
multi-task deep models can discover an effective aesthetic representation to
achieve state-of-the-art results.Comment: 13 pages, 10 figure
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Remarkable progress has been made in image recognition, primarily due to the
availability of large-scale annotated datasets and the revival of deep CNN.
CNNs enable learning data-driven, highly representative, layered hierarchical
image features from sufficient training data. However, obtaining datasets as
comprehensively annotated as ImageNet in the medical imaging domain remains a
challenge. There are currently three major techniques that successfully employ
CNNs to medical image classification: training the CNN from scratch, using
off-the-shelf pre-trained CNN features, and conducting unsupervised CNN
pre-training with supervised fine-tuning. Another effective method is transfer
learning, i.e., fine-tuning CNN models pre-trained from natural image dataset
to medical image tasks. In this paper, we exploit three important, but
previously understudied factors of employing deep convolutional neural networks
to computer-aided detection problems. We first explore and evaluate different
CNN architectures. The studied models contain 5 thousand to 160 million
parameters, and vary in numbers of layers. We then evaluate the influence of
dataset scale and spatial image context on performance. Finally, we examine
when and why transfer learning from pre-trained ImageNet (via fine-tuning) can
be useful. We study two specific computer-aided detection (CADe) problems,
namely thoraco-abdominal lymph node (LN) detection and interstitial lung
disease (ILD) classification. We achieve the state-of-the-art performance on
the mediastinal LN detection, with 85% sensitivity at 3 false positive per
patient, and report the first five-fold cross-validation classification results
on predicting axial CT slices with ILD categories. Our extensive empirical
evaluation, CNN model analysis and valuable insights can be extended to the
design of high performance CAD systems for other medical imaging tasks
Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO
The exquisite sensitivity of the advanced LIGO detectors has enabled the
detection of multiple gravitational wave signals. The sophisticated design of
these detectors mitigates the effect of most types of noise. However, advanced
LIGO data streams are contaminated by numerous artifacts known as glitches:
non-Gaussian noise transients with complex morphologies. Given their high rate
of occurrence, glitches can lead to false coincident detections, obscure and
even mimic gravitational wave signals. Therefore, successfully characterizing
and removing glitches from advanced LIGO data is of utmost importance. Here, we
present the first application of Deep Transfer Learning for glitch
classification, showing that knowledge from deep learning algorithms trained
for real-world object recognition can be transferred for classifying glitches
in time-series based on their spectrogram images. Using the Gravity Spy
dataset, containing hand-labeled, multi-duration spectrograms obtained from
real LIGO data, we demonstrate that this method enables optimal use of very
deep convolutional neural networks for classification given small training
datasets, significantly reduces the time for training the networks, and
achieves state-of-the-art accuracy above 98.8%, with perfect precision-recall
on 8 out of 22 classes. Furthermore, new types of glitches can be classified
accurately given few labeled examples with this technique. Once trained via
transfer learning, we show that the convolutional neural networks can be
truncated and used as excellent feature extractors for unsupervised clustering
methods to identify new classes based on their morphology, without any labeled
examples. Therefore, this provides a new framework for dynamic glitch
classification for gravitational wave detectors, which are expected to
encounter new types of noise as they undergo gradual improvements to attain
design sensitivity
Auto Deep Compression by Reinforcement Learning Based Actor-Critic Structure
Model-based compression is an effective, facilitating, and expanded model of
neural network models with limited computing and low power. However,
conventional models of compression techniques utilize crafted features [2,3,12]
and explore specialized areas for exploration and design of large spaces in
terms of size, speed, and accuracy, which usually have returns Less and time is
up. This paper will effectively analyze deep auto compression (ADC) and
reinforcement learning strength in an effective sample and space design, and
improve the compression quality of the model. The results of compression of the
advanced model are obtained without any human effort and in a completely
automated way. With a 4- fold reduction in FLOP, the accuracy of 2.8% is higher
than the manual compression model for VGG-16 in ImageNet
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