Unifying domain adaptation and self-supervised learning for CXR segmentation via AdaIN-based knowledge distillation

As the segmentation labels are scarce, extensive researches have been conducted to train segmentation networks without labels or with only limited labels. In particular, domain adaptation, self-supervised learning, and teacher-student architecture have been intro- duced to distill knowledge from various tasks to improve the segmentation performance. However, these approaches appear different from each other, so it is not clear how these seemingly different approaches can be combined for better performance. Inspired by the recent StarGANv2 for multi-domain image translation, here we propose a novel seg- mentation framework via AdaIN-based knowledge distillation, where a single generator with AdaIN layers is trained along with the AdaIN code generator and style encoder so that the generator can perform both domain adaptation and segmentation. Specifically, our framework is designed to deal with difficult situations in chest X-ray (CXR) seg- mentation tasks where segmentation masks are only available for normal CXR data, but the trained model should be applied for both normal and abnormal CXR images. Since a single generator is used for abnormal to normal domain conversion and segmentation by simply changing the AdaIN codes, the generator can synergistically learn the com- mon features to improve segmentation performance. Experimental results using CXR data confirm that the trained network can achieve the state-of-the art segmentation per- formance for both normal and abnormal CXR images.

Severity Quantification and Lesion Localization of COVID-19 on CXR using Vision Transformer

Under the global pandemic of COVID-19, building an automated framework that quantifies the severity of COVID-19 and localizes the relevant lesion on chest X-ray images has become increasingly important. Although pixel-level lesion severity labels, e.g. lesion segmentation, can be the most excellent target to build a robust model, collecting enough data with such labels is difficult due to time and labor-intensive annotation tasks. Instead, array-based severity labeling that assigns integer scores on six subdivisions of lungs can be an alternative choice enabling the quick labeling. Several groups proposed deep learning algorithms that quantify the severity of COVID-19 using the array-based COVID-19 labels and localize the lesions with explainability maps. To further improve the accuracy and interpretability, here we propose a novel Vision Transformer tailored for both quantification of the severity and clinically applicable localization of the COVID-19 related lesions. Our model is trained in a weakly-supervised manner to generate the full probability maps from weak array-based labels. Furthermore, a novel progressive self-training method enables us to build a model with a small labeled dataset. The quantitative and qualitative analysis on the external testset demonstrates that our method shows comparable performance with radiologists for both tasks with stability in a real-world application.

Vision Transformer for COVID-19 CXR Diagnosis using Chest X-ray Feature Corpus

Under the global COVID-19 crisis, developing robust diagnosis algorithm for COVID-19 using CXR is hampered by the lack of the well-curated COVID-19 data set, although CXR data with other disease are abundant. This situation is suitable for vision transformer architecture that can exploit the abundant unlabeled data using pre-training. However, the direct use of existing vision transformer that uses the corpus generated by the ResNet is not optimal for correct feature embedding. To mitigate this problem, we propose a novel vision Transformer by using the low-level CXR feature corpus that are obtained to extract the abnormal CXR features. Specifically, the backbone network is trained using large public datasets to obtain the abnormal features in routine diagnosis such as consolidation, glass-grass opacity (GGO), etc. Then, the embedded features from the backbone network are used as corpus for vision transformer training. We examine our model on various external test datasets acquired from totally different institutions to assess the generalization ability. Our experiments demonstrate that our method achieved the state-of-art performance and has better generalization capability, which are crucial for a widespread deployment.

Deep Learning COVID-19 Features on CXR using Limited Training Data Sets

Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of the CXR data set for deep neural network training is difficult. To address this problem, here we propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. The proposed method is inspired by our statistical analysis of the potential imaging biomarkers of the CXR radiographs. Experimental results show that our method achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage.