68 research outputs found
Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs
Chest X-ray is one of the most accessible medical imaging technique for
diagnosis of multiple diseases. With the availability of ChestX-ray14, which is
a massive dataset of chest X-ray images and provides annotations for 14
thoracic diseases; it is possible to train Deep Convolutional Neural Networks
(DCNN) to build Computer Aided Diagnosis (CAD) systems. In this work, we
experiment a set of deep learning models and present a cascaded deep neural
network that can diagnose all 14 pathologies better than the baseline and is
competitive with other published methods. Our work provides the quantitative
results to answer following research questions for the dataset: 1) What loss
functions to use for training DCNN from scratch on ChestX-ray14 dataset that
demonstrates high class imbalance and label co occurrence? 2) How to use
cascading to model label dependency and to improve accuracy of the deep
learning model?Comment: Submitted to CVPR 201
Deep learning classification of chest x-ray images
We propose a deep learning based method for classification of commonly
occurring pathologies in chest X-ray images. The vast number of publicly
available chest X-ray images provides the data necessary for successfully
employing deep learning methodologies to reduce the misdiagnosis of thoracic
diseases. We applied our method to the classification of two example
pathologies, pulmonary nodules and cardiomegaly, and we compared the
performance of our method to three existing methods. The results show an
improvement in AUC for detection of nodules and cardiomegaly compared to the
existing methods.Comment: 4 pages, 4 figures, 2 tables, conference , SSIAI 202
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