Keywords:
Metastases, Atelectasis, Screening, Computer Applications-Detection, diagnosis, Neural networks, Conventional radiography, CAD, Thorax, Lung, Cardiac
Authors:
N. Ramanauskas, J. Dementaviciene, J. Bialopetravičius, D. Barušauskas, J. Armaitis, J. Stankeviciene, G. Danys, R. Puronaite, R. Kizlaitis; Vilnius/LT
DOI:
10.1594/ecr2018/C-1896
Aims and objectives
Chest X-Ray (CXR) is one of the most commonly performed radiological exams.
Various computer-assisted diagnostic (CAD) algorithms have already been proposed for automated CXR pathology classification.
However,
the use of CXR CAD tools is not yet widespread in a clinical setting.
An ideal algorithm should differentiate between a variety of CXR pathologies that manifest similarly in appearance and be robust with respect to different CXR images.
Using a large and inhomogeneous dataset from 5 medical centers we aim to build an end-to-end model for multiclass CXR pathology classification of the most common radiological CXR findings including radiological signs of tuberculosis.
We focus on robustness with respect to different devices and patients.
Despite a rising interest in CXR CAD,
the maturity of studies for this task is still behind the fields of skin cancer diagnosis [1] or retinopathy [2],
where the biggest advances have recently occured.
This work is a stepping stone towards large cross-institutional trials for CXR CAD.