Name
Abstract | ||
|---|---|---|
Automatic tissue classification from medical images is an important step in pathology detection and diagnosis. Here, we deal with mammography images and present a novel supervised deep learning-based framework for region classification into semantically coherent tissues. The proposed method uses Convolutional Neural Network (CNN) to learn discriminative features automatically. We overcome the difficulty involved in a medium-size database by training the CNN in an overlapping patch-wise manner. In order to accelerate the pixel-wise automatic class prediction, we use convolutional layers instead of the classical fully connected layers. This approach results in significantly faster computation, while preserving the classification accuracy. The proposed method was tested on annotated mammography images and demonstrates promising image segmentation and tissue classification results. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1080/21681163.2015.1131197 | COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION |
Keywords | Field | DocType |
Digital mammography, multi-region segmentation, deep neural networks | Digital mammography,Mammography,Computer science,Convolutional neural network,Image segmentation,Artificial intelligence,Deep learning,Discriminative model,Machine learning,Deep neural networks,Computation | Journal |
Volume | Issue | ISSN |
6 | 3 | 2168-1163 |
Citations | PageRank | References |
8 | 0.42 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anastasia Dubrovina | 1 | 85 | 5.87 |
| Pavel Kisilev | 2 | 97 | 12.41 |
| Ginsburg, Boris | 3 | 75 | 8.77 |
| Sharbell Y. Hashoul | 4 | 13 | 1.58 |
| Ron Kimmel | 5 | 2262 | 159.14 |