We present a weakly supervised deep learning model for classifying thoracic diseases and identifying abnormalities in chest radiography. In this work, instead of learning from medical imaging data with region-level annotations, our model was merely trained on imaging data with image-level labels to classify diseases, and is able to identify abnormal image regions simultaneously. Our model consists of a customized pooling structure and an adaptive DenseNet front-end, which can effectively recognize possible disease features for classification and localization tasks. Our method has been validated on the publicly available ChestX-ray14 dataset. Experimental results have demonstrated that our classification and localization prediction performance achieved significant improvement over the previous models on the ChestX-ray14 dataset. In summary, our network can produce accurate disease classification and localization, which can potentially support clinical decisions.
The chest radiography is the most ordered and common radiological examination for chest diseases. It is a low-cost, low radiation and fast imaging exam. Recent studies also demonstrated the chest radiography's application on detection and evaluation of coronary artery diseases, which are usually evaluated using Computed Tomography (CT) with expensive cost and high radiation dose @cite_21 @cite_24 @cite_9 . CAD techniques have been widely applied in chest radiography for task such as automatic diagnosis and patient image retrieval. Previously, Bar al adapted the Decaf-Net for 8 thoracic diseases classification on a relatively small chest x-ray dataset @cite_12 . Lajhani al proposed the DCNN for tuberculosis classification task. Their model ensembled both Alex-Net and Google-Net and achieved impressive results @cite_26 . Anavi al has worked on image retrieval in medicine, specifically for chest radiography given the pathology @cite_3 . All the aforementioned research showed promising results. However, they only conducted their experiments on relatively small dataset, ranging from 10 to 500 images.
@cite_26 @cite_9 @cite_21 @cite_3 @cite_24 @cite_12
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Deep learning with convolutional neural networks can accurately classify tuberculosis at chest radiography with an area under the curve of 0.99. Abstract We have developed a technique to image coronary calcium, an excellent biomarker for atherosclerotic disease, using low cost, low radiation dual energy (DE) chest radiography, with potential for widespread screening from an already ordered exam. Our dual energy coronary calcium (DECC) processing method included automatic heart silhouette segmentation, sliding organ registration and scatter removal to create a bone-image-like, coronary calcium image with significant reduction in motion artifacts and improved calcium conspicuity compared to standard, clinically available DE processing. Experiments with a physical dynamic cardiac phantom showed that DECC processing reduced 73 of misregistration error caused by cardiac motion over a wide range of heart rates and x-ray radiation exposures. Using the functional measurement test (FMT), we determined significant image quality improvement in clinical images with DECC processing (p 50 as the gold standard for an actual positive test result. AUC performance was significantly improved from 0.73 ± 0.14 with standard DE to 0.87 ± 0.10 with DECC (p = 0.0095) for this limited set of surgical patient data biased towards heavy calcifications. The proposed DECC processing shows good potential for coronary calcium detection in DE chest radiography, giving impetus for a larger clinical evaluation. In this work various approaches are investigated for X-ray image retrieval and specifically chest pathology retrieval. Given a query image taken from a data set of 443 images, the objective is to rank images according to similarity. Different features, including binary features, texture features, and deep learning (CNN) features are examined. In addition, two approaches are investigated for the retrieval task. One approach is based on the distance of image descriptors using the above features (hereon termed the "descriptor"-based approach); the second approach ("classification"-based approach) is based on a probability descriptor, generated by a pair-wise classification of each two classes (pathologies) and their decision values using an SVM classifier. Best results are achieved using deep learning features in a classification scheme. Coronary artery calcification (CAC) as assessed with CT calcium score is the best biomarker for predicting future cardiac events. Dual energy (DE) chest radiography offers an inexpensive, low radiation-dose alternative to CT. We have shown CAC can be visualized using non-gated, 2-shot, DE chest x-ray imaging. However, calcium signal from the ribs, superimposed on the CAC signal, can interfere with both image registration and reader detection of CAC. To improve the registration algorithm and the detectability of CAC, we created an enhanced CAC visualization algorithm with automatic rib segmentation and suppression. In this paper, we trained an active appearance model to detect the spatial location of rib in the DE bone images. Then, we suppressed the rib signal in the single kVp images using a kernel-filter based method. After the rib suppression, we used affine and non-rigid registration to align the CAC in low and high kVp image. 1800 dual energy cases were used in our experiment after data augmentation on 60 cases. We evaluated the segmentation from the rib AAM using six-fold cross validation. The average dice coefficient of the rib segmentation in the heart region is 0.92. The reduction of CAC mis-registration by the enhanced CAC registration algorithm is evaluated using both simulated and clinical images. Adding rib suppression improves the image registration significantly. Our results show that in both simulated synthetic images and clinical images, the CAC overlap improves from 0p to > 50p after rib suppression. Results suggest that CAC visualization and registration can be significantly improved using the rib suppression when rib signal confounds CAC signal in DE chest radiography. In this work, we examine the strength of deep learning approaches for pathology detection in chest radiographs. Convolutional neural networks (CNN) deep architecture classification approaches have gained popularity due to their ability to learn mid and high level image representations. We explore the ability of CNN learned from a non-medical dataset to identify different types of pathologies in chest x-rays. We tested our algorithm on a 433 image dataset. The best performance was achieved using CNN and GIST features. We obtained an area under curve (AUC) of 0.87–0.94 for the different pathologies. The results demonstrate the feasibility of detecting pathology in chest x-rays using deep learning approaches based on non-medical learning. This is a first-of-its-kind experiment that shows that Deep learning with ImageNet, a large scale non-medical image database may be a good substitute to domain specific representations, which are yet to be available, for general medical image recognition tasks.