Corpus ID: 8865011

Medical Image Deep Learning with Hospital PACS Dataset

@article{Cho2015MedicalID,
  title={Medical Image Deep Learning with Hospital PACS Dataset},
  author={Junghwan Cho and Kyewook Lee and Ellie Shin and Garry Choy and Synho Do},
  journal={ArXiv},
  year={2015},
  volume={abs/1511.06348}
}
The use of Convolutional Neural Networks (CNN) in natural im age classification systems has produced very impressive results. [...] Key Method The CNN was applied to classify axial Computed Tomography (CT) imag es into six anatomical classes. We trained the CNN using six different sizes of training data set ( 5, 10, 20, 50, 100, and200) and then tested the resulting system with a total of 6000 CT images.Expand
A deep learning architecture for classifying medical images of anatomy object
  • S. Khan, S. Yong
  • Computer Science
  • 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
  • 2017
TLDR
Based on the experiments, it is shown that the proposed Convolutional Neural Network architecture outperforms the three milestone architectures in classifying medical images of anatomy object. Expand
High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks
TLDR
A deep convolutional neural network created using non-radiological images, and an augmented set of radiographs is effective in highly accurate classification of chest radiograph view type and is a feasible, rapid method for high-throughput annotation. Expand
Training Set Size for Skin Cancer Classification Using Google's Inception v3
Today, computer aided diagnosis (CADx) is a common occurrence in hospitals. With image recognition, computers are able to detect signs of breast cancer and different kinds of lung diseases. For aExpand
Automated Bone Age Classification with Deep Neural Networks
In this paper we look at the use of Convolutional Neural Network methods to train a model to predict developmental bone age of a patient given x-ray images. We use the Digital Hand Atlas datasetExpand
Detection of Hepatocellular Carcinoma in CT Images Using Deep Learning
TLDR
V validity and usefulness of the learning and recognition programs were proved by examining the calculated results and the hepatocellular carcinoma could be detected with relatively high sensitivity of 92.2% even with a relatively small number of learning data, namely 1,200 sets of CT. Expand
Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification
TLDR
Investigation of cutting-edge deep learning methods for information extraction from medical imaging free text reports at a multi-institutional scale and compares them to the state-of-the-art domain-specific rule-based system - PEFinder and traditional machine learning methods - SVM and Adaboost suggests feasibility of broader usage of neural network models in automated classification of multi-Institutional imaging text reports. Expand
Anatomical region identification in medical X-ray computed tomography (CT) scans: development and comparison of alternative data analysis and vision-based methods
TLDR
Three methods were implemented and compared to divide medical X-ray computed tomography images into six main anatomical regions: head, neck, chest, abdomen, pelvis and legs, and an analytical approach achieved acceptable accuracy for anatomical region segmentation without the need for explicit data labeling. Expand
Differential Data Augmentation Techniques for Medical Imaging Classification Tasks
TLDR
It is shown that the extent to which an augmented training set retains properties of the original medical images determines model performance, and that the augmentation affects mass generation. Expand
NLM at ImageCLEF 2017 Caption Task
This paper describes the participation of the U.S. National Library of Medicine (NLM) in the ImageCLEF 2017 caption task. We proposed different machine learning methods using training subsets that weExpand
Developing and Deploying Machine Learning Pipelines against Real-Time Image Streams from the PACS
TLDR
Niffler is an integrated ML framework that runs in research clusters that receives radiology images in real-time from hospitals’ Picture Archiving and Communication Systems (PACS) and provides metadata extraction capabilities and Application programming interfaces (APIs) to apply filters on the DICOM images and run the ML pipelines. Expand
...
1
2
3
...

References

SHOWING 1-10 OF 18 REFERENCES
Anatomy-specific classification of medical images using deep convolutional nets
TLDR
It is demonstrated that deep learning can be used to train very reliable and accurate classifiers that could initialize further computer-aided diagnosis and a data augmentation approach can help to enrich the data set and improve classification performance. Expand
Computer-aided classification of lung nodules on computed tomography images via deep learning technique
TLDR
This study attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques and introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images. Expand
Computer-aided diagnosis and artificial intelligence in clinical imaging.
TLDR
A nonlinear image warping technique for matching the previous image to the current one has been developed and one element of CAD, temporal subtraction, has been applied for enhancing interval changes and for suppressing unchanged structures between 2 successive radiologic images. Expand
Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation
TLDR
Experimental results show the ability of ConvNets to generalize well to different medical imaging CADe applications and scale elegantly to various data sets. Expand
Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks
TLDR
Experimental results provide initial evidence that convolutional neural networks have the potential to extract PET imaging representations that are highly predictive of response to therapy, and compare the performance of two competing radiomics strategies. 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
Visualizing and Understanding Convolutional Networks
TLDR
A novel visualization technique is introduced that gives insight into the function of intermediate feature layers and the operation of the classifier in large Convolutional Network models, used in a diagnostic role to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark. Expand
Very Deep Convolutional Networks for Large-Scale Image Recognition
TLDR
This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. Expand
Caffe: Convolutional Architecture for Fast Feature Embedding
TLDR
Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures. Expand
Predicting sample size required for classification performance
TLDR
A simple and effective sample size prediction algorithm that conducts weighted fitting of learning curves and outperformed an un-weighted algorithm described in previous literature can help researchers determine annotation sample size for supervised machine learning. Expand
...
1
2
...