Chapter title |
Classification of Pancreatic Cysts in Computed Tomography Images Using a Random Forest and Convolutional Neural Network Ensemble
|
---|---|
Chapter number | 18 |
Book title |
Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017
|
Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, September 2017
|
DOI | 10.1007/978-3-319-66179-7_18 |
Pubmed ID | |
Book ISBNs |
978-3-31-966178-0, 978-3-31-966179-7
|
Authors |
Konstantin Dmitriev, Arie E. Kaufman, Ammar A. Javed, Ralph H. Hruban, Elliot K. Fishman, Anne Marie Lennon, Joel H. Saltz |
Abstract |
There are many different types of pancreatic cysts. These range from completely benign to malignant, and identifying the exact cyst type can be challenging in clinical practice. This work describes an automatic classification algorithm that classifies the four most common types of pancreatic cysts using computed tomography images. The proposed approach utilizes the general demographic information about a patient as well as the imaging appearance of the cyst. It is based on a Bayesian combination of the random forest classifier, which learns subclass-specific demographic, intensity, and shape features, and a new convolutional neural network that relies on the fine texture information. Quantitative assessment of the proposed method was performed using a 10-fold cross validation on 134 patients and reported a classification accuracy of 83.6%. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 37 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 7 | 19% |
Student > Bachelor | 4 | 11% |
Student > Ph. D. Student | 4 | 11% |
Student > Master | 3 | 8% |
Other | 3 | 8% |
Other | 6 | 16% |
Unknown | 10 | 27% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 11 | 30% |
Engineering | 3 | 8% |
Computer Science | 3 | 8% |
Environmental Science | 2 | 5% |
Biochemistry, Genetics and Molecular Biology | 1 | 3% |
Other | 2 | 5% |
Unknown | 15 | 41% |