Breast cancer is the most common malignancy and leading cause of cancer deaths in women around the world. The preferred diagnostic and prevention method used to fight breast cancer is mammography, being effective, cheap, reliable and suitable for screening large populations. The analysis of mammograms requires considerable effort from qualified radiologists, who have been increasingly relying on computer-aided diagnosis and the development of tools that require little supervision could save the lives of hundreds of thousands of women across the world.

In recent years, the integration of Artificial Intelligence (AI) in the field of medicine has brought about a significant transformation in healthcare delivery. Computer vision technology finds itself at the forefront of this proces. Its applications to mamography data represent an active research field, stimulated by the fact that among the clinical imaging modalities, mammography stands out in terms of high spatial resolution (full-field digital mammography systems usually produce images at resolutions ranging from 1920×2304 to 4708×5844 pixels.

I will discuss research work stemming from projects conducted at the Institute for Artificial Intelligence Research and Development of Serbia, aimed at implementing AI-assisted mamography screening at the national level. Viewed through the lens of medical applications, the talk will cover the evolution of state-of-the-art research in the domain of computer vision and multimodal AI over the last 5 years, from the applications of “classical” convolutional neural networks and visual transformer models, explainable AI extensions, generative (diffusion) models, to self supervised and multiple instance learning approaches to address specific challenges and (downstream) tasks such as: image classification, lesion detection and synthetic mammogram generation.