Recent advances in deep learning have led to a promising performance in many medical
image analysis tasks. As the most commonly performed radiological exam, chest …

Due to the augmented rise of COVID-19, clinical specialists are looking for fast faultless
diagnosis strategies to restrict Covid spread while attempting to lessen the computational …

Clusters of viral pneumonia occurrences over a short period may be a harbinger of an
outbreak or pandemic. Rapid and accurate detection of viral pneumonia using chest X-rays …

The novel coronavirus disease 2019 (COVID‐19) is a contagious disease that has caused
thousands of deaths and infected millions worldwide. Thus, various technologies that allow …

In this paper, we propose a vision-language pre-training model, Clinical-BERT, for the
medical domain, and devise three domain-specific tasks: Clinical Diagnosis (CD), Masked …

Multi-label classification (MLC) can attach multiple labels on single image, and has
achieved promising results on medical images. But existing MLC methods still face …

Medical anomaly detection is a crucial yet challenging task aimed at recognizing abnormal
images to assist in diagnosis. Due to the high-cost annotations of abnormal images, most …

Large-scale datasets with high-quality labels are desired for training accurate deep learning
models. However, due to the annotation cost, datasets in medical imaging are often either …

Automatic medical report generation (MRG) is of great research value as it has the potential
to relieve radiologists from the heavy burden of report writing. Despite recent advancements …

Chest radiography is a relatively cheap, widely available medical procedure that conveys
key information for making diagnostic decisions. Chest X-rays are frequently used in the …