One of the major problems in medical imaging is the shortage of pathology data. In most cases, the acquisition of labeled data is expensive and usually involves manual labeling by a skilled medical expert. Because of this, most medical imaging tasks suffer from a severe class imbalance with a bias towards non-pathological classes, resulting in reduced performance. The recent growth in the use of generative adversarial networks and their ability to generate synthetic data shows great promise for reducing the class imbalance problem. In this work we introduce the GC-CycleGAN model, a general method for CycleGAN factorization, utilizing Grad-CAMs as auxiliary data in the CycleGAN model to generate synthetic images. Our novel approach utilizes Grad-CAMs ability to describe class activation and uses it for improved network classification, rather than as a visualization tool. The spread of the COVID-19 pandemic is affecting the lives of millions worldwide. If proven effective, automated COVID-19 detection from chest X-ray images can be a supportive step in the fight against COVID-19. However, the task of COVID-19 classification suffers greatly from the class imbalance problem. Using the GC-CycleGAN method, we demonstrate in this work the ability to balance a heavily imbalanced dataset for the task of COVID-19 vs. non-COVID-19 pneumonia X-ray classification. We show improved results over two baselines and the COVID-Net model.
Medical image segmentation has a fundamental role in many computer-aided diagnosis (CAD) applications. Accurate segmentation of medical images is a key step in tracking changes over time, contouring during radiotherapy planning, and more. One of the state-of-the-art models for medical image segmentation is the U–Net that consists of an encoder-decoder based architecture. Many variations exist to the U–Net architecture. In this work, we present a new training procedure that combines U–Net with an adversarial training we refer to as Adversarial U–Net. We show that Adversarial U–Net outperformes the conventional U–Net in three versatile domains that differ in the acquisition method as well as the physical characteristics and yields smooth and improved segmentation maps.
Pulmonary embolus (PE) refers to obstruction of pulmonary arteries by blood clots. PE accounts for approximately 100,000 deaths per year in the United States alone. The clinical presentation of PE is often nonspecific, making the diagnosis challenging. Thus, rapid and accurate risk stratification is of paramount importance. High-risk PE is caused by right ventricular (RV) dysfunction from acute pressure overload, which in return can help identify which patients require more aggressive therapy. Reconstructed four-chamber views of the heart on chest CT can detect right ventricular enlargement. CT pulmonary angiography (CTPA) is the golden standard in the diagnostic workup of suspected PE. Therefore, it can link between diagnosis and risk stratification strategies. We developed a weakly supervised deep learning algorithm, with an emphasis on novel a attention mechanism, to automatically classify RV strain on CTPA. Our method is a 3D residual block-based model with integrated attention blocks. We evaluated our model on a dataset of CTPAs of emergency department (ED) PE patients. Our results show that attention consistently improves prediction. We infer that unmarked CTPAs can be used for effective RV strain classification. This could be used as a second reader, alerting for high-risk PE patients. To the best of our knowledge, there are no previous deep learning-based studies that attempted to solve this problem.
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