Visual Analytics of a Computer-Aided Diagnosis System for Pancreatic Lesions

Machine learning is a powerful and effective tool for medical image analysis to perform computer-aided diagnosis (CAD). Having great potential in improving the accuracy of a diagnosis, CAD systems are often analyzed in terms of the final accuracy, leading to a limited understanding of the internal d...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2021-03, Vol.27 (3), p.2174-2185
Main Authors: Dmitriev, Konstantin, Marino, Joseph, Baker, Kevin, Kaufman, Arie E.
Format: Article
Language:English
Subjects:
abdominal imaging
Adult
Algorithms
Analytical models
Artificial neural networks
Cancer
Classification
Computed tomography
computer-aided diagnosis
Decision analysis
Decision making
Decision trees
Diagnosis
Female
Humans
Image analysis
Image Interpretation, Computer-Assisted - methods
Lesions
Machine Learning
Male
Medical imaging
Neural Networks, Computer
pancreas
Pancreas - diagnostic imaging
Pancreatic Neoplasms - diagnostic imaging
Radiography, Abdominal
Radiomics
Solid modeling
Visual aids
Visualization
Young Adult
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Summary:Machine learning is a powerful and effective tool for medical image analysis to perform computer-aided diagnosis (CAD). Having great potential in improving the accuracy of a diagnosis, CAD systems are often analyzed in terms of the final accuracy, leading to a limited understanding of the internal decision process, impossibility to gain insights, and ultimately to skepticism from clinicians. We present a visual analytics approach to uncover the decision-making process of a CAD system for classifying pancreatic cystic lesions. This CAD algorithm consists of two distinct components: random forest (RF), which classifies a set of predefined features, including demographic features, and a convolutional neural network (CNN), which analyzes radiological (imaging) features of the lesions. We study the class probabilities generated by the RF and the semantical meaning of the features learned by the CNN. We also use an eye tracker to better understand which radiological features are particularly useful for a radiologist to make a diagnosis and to quantitatively compare with the features that lead the CNN to its final classification decision. Additionally, we evaluate the effects and benefits of supplying the CAD system with a case-based visual aid in a second-reader setting.
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2019.2947037