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Deep Neural Networks for Image-Based Dietary Assessment
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Toward Content Based Image Retrieval with Deep Convolutional Neural Networks.

Judah E S Sklan1, Andrew J Plassard1, Daniel Fabbri2

  • 1Computer Science, Vanderbilt University, Nashville, TN, USA 37235.

Proceedings of Spie--The International Society for Optical Engineering
|April 28, 2015
PubMed
Summary
This summary is machine-generated.

Applying deep Convolutional Neural Networks (dCNN) for content-based image retrieval in medicine showed limited success. Further research is needed to adapt these machine learning techniques for effective medical image analysis and classification.

Keywords:
content based image retrievaldeep convolutional neural networksmedical imagesunsupervised learning

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Computer Science

Background:

  • Content-based image retrieval (CBIR) has advanced significantly for general images using deep Convolutional Neural Networks (dCNN).
  • CBIR in medicine could aid in identifying similar cases, understanding rare diseases, and enhancing patient care.
  • Previous dCNN applications have shown success with general photographic images.

Purpose of the Study:

  • To investigate the application of a leading ImageNet CBIR technique to clinical medical images.
  • To evaluate the effectiveness of a custom-built dCNN for classifying magnetic resonance (MR) and computed tomography (CT) scans.
  • To assess the potential of dCNNs for content-based image retrieval in a clinical setting.

Main Methods:

  • A dCNN with four hidden layers was constructed, reducing image dimensionality.
  • The network was trained using back-propagation on 1 million MR and CT images.
  • Classifiers were evaluated on 2100 independently labeled images projected into manifold space.

Main Results:

  • The study achieved a preliminary true positive rate of approximately 20% for medical image classification.
  • Quantitative results were lower than anticipated, indicating challenges in direct adaptation of ImageNet techniques.
  • Analysis suggested potential improvements with more balanced data sampling and refined label structures.

Conclusions:

  • Direct adaptation of ImageNet CBIR techniques to clinical medical images yielded disappointing preliminary results.
  • The study highlights the need for further research and adaptation of machine learning models for medical image analysis.
  • Despite limitations, the preliminary effort shows promise for automated medical image classification with further development.