… Classification performance on the external dataset showed … for normal versus pneumonia
classification. Pretraining with … differentiate normal and abnormal chest radiographs, thereby …

… both normal, and abnormal images with lung opacities that are not related to pneumonia.
This is distinct from our goal that aims to classify CXRs as normal versus abnormal, with the …

… [37] MF McNitt-Gray, HK Huang, and JW Sayre, “Feature selection in the pattern
classification problem of digital chest radiograph segmentation,” IEEE Trans. Med. Imag., vol. …

… abnormalities and classification of them. Automated abnormality detection on chest radiographs
… diseases and the limited quality of chest radiographs. Manually marking the counters of …

… multiple abnormalities based on chest radiography of the … 25 radiologists on 50 chest
radiographs leads to a moderate … As such the interpretation on radiographs is often subjective …

… classification tasks. In this paper, we study the usefulness of TA and GA for classifying abnormal
chest X… Then, we trained an abnormality classifier using three training sets individually – …

… This data set was used to train CNNs to classify chest radiographs as normal or abnormal
before evaluation on a held-out set of 533 images hand-labeled by expert radiologists. The …

… Our work differs from the aforementioned papers in that, in addition to evaluating the
effectiveness of our models for abnormality detection in chest radiographs, we systematically …

… chest radiographs, the most frequently performed medical imaging study. We hypothesize
CNNs can learn to classify frontal chest radiographs … abnormalities on chest radiographs and …

… Classification performance on the external dataset showed … for normal versus pneumonia
classification. Pretraining with … differentiate normal and abnormal chest radiographs, thereby …