Corpus ID: 21693710

Survey of Deep Learning Applications to Medical Image Analysis

@inproceedings{Suzuki2017SurveyOD,
  title={Survey of Deep Learning Applications to Medical Image Analysis},
  author={Kenji Suzuki},
  year={2017}
}
Recently, a machine learning (ML) area called deep learning emerged in the computer-vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide com putervision competition, ImageNet Classification. Since then, researchers in many fields, including medical image analysis, have started actively participating in the explosively growing… Expand

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References

SHOWING 1-10 OF 84 REFERENCES
A Transfer Learning Method with Deep Convolutional Neural Network for Diffuse Lung Disease Classification
We introduce a deep convolutional neural network (DCNN) as feature extraction method in a computer aided diagnosis (CAD) system in order to support diagnosis of diffuse lung diseases (DLD) onExpand
Pixel-Based Machine Learning in Medical Imaging
  • Kenji Suzuki
  • Computer Science, Medicine
  • Int. J. Biomed. Imaging
  • 2012
TLDR
PMLs are surveyed to make clear classes ofPMLs, similarities and differences within (among) different PMLs and those between PMLS and feature-based MLs, advantages and limitations of PML’s, and their applications in medical imaging. Expand
ImageNet classification with deep convolutional neural networks
TLDR
A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective. Expand
Machine Learning in Medical Imaging
TLDR
This paper proposes a hierarchical ensemble classification algorithm to gradually combine the features and decisions into a unified model for more accurate classification of brain images for Alzheimer's disease diagnosis. Expand
Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks
TLDR
It was showed that the proposed multi-view ConvNets is highly suited to be used for false positive reduction of a CAD system. Expand
DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation
TLDR
This paper presents a probabilistic bottom-up approach for pancreas segmentation in abdominal computed tomography CT scans, using multi-level deep convolutional networks ConvNets, and proposes and evaluates several variations of deep ConvNETS in the context of hierarchical, coarse-to-fine classification on image patches and regions, i.e. superpixels. Expand
A New 2.5D Representation for Lymph Node Detection Using Random Sets of Deep Convolutional Neural Network Observations
TLDR
This work operates a preliminary candidate generation stage, towards -100% sensitivity at the cost of high FP levels (-40 per patient), to harvest volumes of interest (VOI), and decomposes any 3D VOI by resampling 2D reformatted orthogonal views N times, via scale, random translations, and rotations with respect to the VOI centroid coordinates. Expand
Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks
TLDR
This paper proposes a novel automatic method to detect CMBs from magnetic resonance (MR) images by exploiting the 3D convolutional neural network (CNN), outperforming previous methods using low-level descriptors or 2D CNNs by a significant margin. Expand
Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets.
TLDR
The authors demonstrated that the DL-CNN can overcome the strong boundary between two regions that have large difference in gray levels and provides a seamless mask to guide level set segmentation, which has been a problem for many gradient-based segmentation methods. Expand
Artificial convolution neural network techniques and applications for lung nodule detection
TLDR
A double-matching method and an artificial visual neural network technique for lung nodule detection that modeled radiologists' reading procedures in order to instruct the artificial neural network to recognize the image patterns predefined and those of interest to experts in radiology. Expand
...
1
2
3
4
5
...