Medical image analysis plays an irreplaceable role in diagnosing, treating, and monitoring
various diseases. Convolutional neural networks (CNNs) have become popular as they can …

Abstract Machine learning (ML) algorithms have made a tremendous impact in the field of
medical imaging. While medical imaging datasets have been growing in size, a challenge …

In this paper, we consider enhancing medical visual-language pre-training (VLP) with
domain-specific knowledge, by exploiting the paired image-text reports from the radiological …

The advent of deep learning has brought great change to the community of computer
science and also revitalized numerous fields where traditional machine learning methods …

Although deep learning models like CNNs have achieved great success in medical image
analysis, the small size of medical datasets remains a major bottleneck in this area. To …

The accurate identification of malignant lung nodules on chest CT is critical for the early
detection of lung cancer, which also offers patients the best chance of cure. Deep learning …

In this work, we present a fully automated lung computed tomography (CT) cancer diagnosis
system, DeepLung. DeepLung consists of two components, nodule detection (identifying the …

Lung cancer is responsible for more fatalities than any other cancer worldwide, with 1.76
million associated deaths reported in 2018. The key issue in the fight against this disease is …

We introduce a new computer aided detection and diagnosis system for lung cancer
screening with low-dose CT scans that produces meaningful probability assessments. Our …

The recent development of imaging and sequencing technologies enables systematic
advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in …