Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks (1705.09850v3)

Abstract: Chest X-Rays (CXRs) are widely used for diagnosing abnormalities in the heart and lung area. Automatically detecting these abnormalities with high accuracy could greatly enhance real world diagnosis processes. Lack of standard publicly available dataset and benchmark studies, however, makes it difficult to compare various detection methods. In order to overcome these difficulties, we have used a publicly available Indiana CXR, JSRT and Shenzhen dataset and studied the performance of known deep convolutional network (DCN) architectures on different abnormalities. We find that the same DCN architecture doesn't perform well across all abnormalities. Shallow features or earlier layers consistently provide higher detection accuracy compared to deep features. We have also found ensemble models to improve classification significantly compared to single model. Combining these insight, we report the highest accuracy on chest X-Ray abnormality detection on these datasets. We find that for cardiomegaly detection, the deep learning method improves the accuracy by a staggering 17 percentage point compared to rule based methods. We applied the techniques to the problem of tuberculosis detection on a different dataset and achieved the highest accuracy. Our localization experiments using these trained classifiers show that for spatially spread out abnormalities like cardiomegaly and pulmonary edema, the network can localize the abnormalities successfully most of the time. One remarkable result of the cardiomegaly localization is that the heart and its surrounding region is most responsible for cardiomegaly detection, in contrast to the rule based models where the ratio of heart and lung area is used as the measure. We believe that through deep learning based classification and localization, we will discover many more interesting features in medical image diagnosis that are not considered traditionally.

Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks

The paper "Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks" presents a detailed investigation into the application of deep convolutional neural networks (DCNs) for abnormality detection and localization in chest X-ray images. The authors focus on enhancing the diagnostic process for heart and lung diseases by harnessing the computational power of DCNs. They emphasize the importance of this research in lightening the burden on radiologists by providing automated, accurate, and quantitative tools for abnormality detection.

Methodology

The paper employs several publicly available datasets, including the Indiana chest X-ray, JSRT, and Shenzhen datasets, each contributing valuable data for training and benchmarking. The authors implement a range of DCN architectures, such as AlexNet, VGG-Net, and ResNet, for classifying abnormalities like cardiomegaly and pulmonary edema. These networks are pre-trained on the ImageNet dataset and then fine-tuned on the medical image data to extract relevant features effectively. Remarkably, the authors find that shallow features outperform deep features, which contrasts with typical image classification tasks where deeper layers often provide more robust results.

Key Findings

A significant finding of the paper is the efficiency of ensemble models over single models in improving classification accuracy. The ensemble models, averaging the predictions from various DCNs, improve the classification accuracy by up to 17 percentage points for cardiomegaly detection compared to traditional rule-based methods. The ensemble approach also achieves the highest accuracy on tuberculosis detection using the Shenzhen dataset, surpassing previously reported results by 5 percentage points.

For localization, the paper applies a sensitivity-based occlusion technique, revealing areas responsible for classification decisions. This approach provides insightful spatial localization for diseases like cardiomegaly and pulmonary edema, mainly highlighting regions critical for diagnosis, such as an enlarged heart in cardiomegaly.

Implications and Future Directions

The results demonstrate the potential of deep learning in advancing medical image diagnostics, especially in automating preliminary diagnostic processes to assist radiologists. The authors suggest that their method can uncover diagnostic features previously unconsidered in traditional methods. However, while certain abnormalities are effectively localized, the method requires refinement for detecting more discrete features, such as nodules or fractures.

Looking forward, expanding datasets and developing more sophisticated models could drive even better accuracy and broader application in various radiological modalities. Furthermore, integrating these models into clinical workflows could revolutionize how chest X-rays are interpreted, providing real-time, reliable diagnostic support.

In conclusion, this paper provides a detailed roadmap of how DCNs can be effectively employed for detecting and localizing chest X-ray abnormalities, setting benchmarks for future studies to compare against and encouraging further exploration into deep learning applications in medical imaging.