Task-Driven Dictionary Learning Based on Mutual Information for Medical Image Classification

Autor: Eyal Klang, Idit Diamant, Eli Konen, Jacob Goldberger, Hayit Greenspan, Michal Amitai
Rok vydání: 2017
Předmět:
Computer science
Feature extraction
Biomedical Engineering
Dictionaries as Topic
02 engineering and technology
Machine learning
computer.software_genre
Sensitivity and Specificity
Pattern Recognition
Automated
030218 nuclear medicine & medical imaging
Machine Learning
03 medical and health sciences
0302 clinical medicine
0202 electrical engineering
electronic engineering
information engineering
Medical imaging
medicine
Humans
Mammography
Visual Word
Training set
medicine.diagnostic_test
Contextual image classification
business.industry
Reproducibility of Results
Pattern recognition
Mutual information
Visualization
Subtraction Technique
Pattern recognition (psychology)
Radiographic Image Interpretation
Computer-Assisted
Radiography
Thoracic
020201 artificial intelligence & image processing
Artificial intelligence
Tomography
X-Ray Computed
business
computer
Zdroj: IEEE Transactions on Biomedical Engineering. 64:1380-1392
ISSN: 1558-2531
0018-9294
DOI: 10.1109/tbme.2016.2605627
Popis: Objective: We present a novel variant of the bag-of-visual-words (BoVW) method for automated medical image classification. Methods: Our approach improves the BoVW model by learning a task-driven dictionary of the most relevant visual words per task using a mutual information-based criterion. Additionally, we generate relevance maps to visualize and localize the decision of the automatic classification algorithm. These maps demonstrate how the algorithm works and show the spatial layout of the most relevant words. Results: We applied our algorithm to three different tasks: chest x-ray pathology identification (of four pathologies: cardiomegaly, enlarged mediastinum, right consolidation, and left consolidation), liver lesion classification into four categories in computed tomography (CT) images and benign/malignant clusters of microcalcifications (MCs) classification in breast mammograms. Validation was conducted on three datasets: 443 chest x-rays, 118 portal phase CT images of liver lesions, and 260 mammography MCs. The proposed method improves the classical BoVW method for all tested applications. For chest x-ray, area under curve of 0.876 was obtained for enlarged mediastinum identification compared to 0.855 using classical BoVW (with p -value $ 0.01). For MC classification, a significant improvement of 4% was achieved using our new approach (with p -value = 0.03). For liver lesion classification, an improvement of 6% in sensitivity and 2% in specificity were obtained (with p -value $ 0.001). Conclusion: We demonstrated that classification based on informative selected set of words results in significant improvement. Significance: Our new BoVW approach shows promising results in clinically important domains. Additionally, it can discover relevant parts of images for the task at hand without explicit annotations for training data. This can provide computer-aided support for medical experts in challenging image analysis tasks.
Databáze: OpenAIRE
Externí odkaz: