XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasets

Autor: Joseph Bullock, Carolina Cuesta-Lazaro, Arnau Quera-Bofarull
Jazyk: angličtina
Rok vydání: 2018
Předmět:
FOS: Computer and information sciences
Computer science
Computer Science - Artificial Intelligence
Computer Vision and Pattern Recognition (cs.CV)
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Computer Science - Computer Vision and Pattern Recognition
FOS: Physical sciences
Image processing
02 engineering and technology
Convolutional neural network
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
0202 electrical engineering
electronic engineering
information engineering
Entropy (information theory)
Segmentation
Cluster analysis
Artificial neural network
business.industry
Deep learning
Pattern recognition
Physics - Medical Physics
Artificial Intelligence (cs.AI)
020201 artificial intelligence & image processing
Artificial intelligence
Medical Physics (physics.med-ph)
F1 score
business
Zdroj: Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging
Popis: X-Ray image enhancement, along with many other medical image processing applications, requires the segmentation of images into bone, soft tissue, and open beam regions. We apply a machine learning approach to this problem, presenting an end-to-end solution which results in robust and efficient inference. Since medical institutions frequently do not have the resources to process and label the large quantity of X-Ray images usually needed for neural network training, we design an end-to-end solution for small datasets, while achieving state-of-the-art results. Our implementation produces an overall accuracy of 92%, F1 score of 0.92, and an AUC of 0.98, surpassing classical image processing techniques, such as clustering and entropy based methods, while improving upon the output of existing neural networks used for segmentation in non-medical contexts. The code used for this project is available online.
11 pages, 5 figures, 2 tables
Databáze: OpenAIRE
Externí odkaz: