Toward Content Based Image Retrieval with Deep Convolutional Neural Networks
By Judah E. S. Sklan, Andrew J. Plassard, Daniel Fabbri, Bennett A. Landman. 2015
49 citations recorded by the source corpus.
Content-based image retrieval (CBIR) offers the potential to identify similar case histories, understand rare disorders, and eventually, improve patient care. Recent advances in database capacity, algorithm efficiency, and deep Convolutional Neural Networks (dCNN), a machine learning technique, have enabled great CBIR success for general photographic images. Here, we investigate applying the leading ImageNet CBIR technique to clinically acquired medical images captured by the Vanderbilt Medical Center. Briefly, we (1) constructed a dCNN with four hidden layers, reducing dimensionality of an input scaled to 128x128 to an output encoded layer of 4x384, (2) trained the network using back-propagation 1 million random magnetic resonance (MR) and computed tomography (CT) images, (3) labeled an independent set of 2100 images, and (4) evaluated classifiers on the projection of the labeled images into manifold space. Quantitative results were disappointing (averaging a true positive rate of only 20%); however, the data suggest that improvements would be possible with more evenly distributed sampling across labels and potential re-grouping of label structures. This preliminary effort at automated classification of medical images with ImageNet is promising, but shows that more work is needed beyond direct adaptation of existing techniques.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Oncology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Computer Vision and Pattern Recognition
In Oncology
Citations in the corpus
- An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “[309] trained a CNN to perform CBIR with more than one million random MR and CT images, with disappointing results (true positive rate of 20%) on their independent test set of 2100 labeled images.”
- Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm (2018, background): “Convolutional neural networks (CNNs), which are the latest, core model of artificial neural networks and deep learning in computer vision, have developed rapidly since roughly 2010 [12].”