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 …

Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence
of disruptive technical advances and impressive experimental results, notably in the field of …

Automatic medical image segmentation is a crucial topic in the medical domain and
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …

Supervised training of deep learning models requires large labeled datasets. There is a
growing interest in obtaining such datasets for medical image analysis applications …

Glaucoma is one of the leading causes of irreversible but preventable blindness in working
age populations. Color fundus photography (CFP) is the most cost-effective imaging …

Causal reasoning can shed new light on the major challenges in machine learning for
medical imaging: scarcity of high-quality annotated data and mismatch between the …

Segmentation of abdominal organs has been a comprehensive, yet unresolved, research
field for many years. In the last decade, intensive developments in deep learning (DL) …

Since the emergence of the Covid-19 pandemic in late 2019, medical imaging has been
widely used to analyze this disease. Indeed, CT-scans of the lungs can help diagnose …

The coronavirus disease 2019 (COVID-19) pandemic is a global health care emergency.
Although reverse-transcription polymerase chain reaction testing is the reference standard …

Breast cancer is one of the leading causes of death among women. Early detection of breast
cancer can significantly improve the lives of millions of women across the globe. Given …