Breast Image Analysis

Special Session 3


Gustavo Carneiro, Andrew Bradley, Jacinto Nascimento, Jaime Cardoso, Neeraj Dhungel, Gabriel Maicas


  • Martyn Nash (Professor at the University of Auckland)
  • Murk Bottema (Associate Professor at Flinders University)
  • Helder Oliveira (Senior Researcher at INESC TEC, University of Porto)


breast cancer, breast imaging, breast image analysis


Current evidence from statistical data suggests that breast cancer is responsible for 23% of all cancer cases and 14% of cancer related deaths amongst women worldwide. Breast imaging and the analysis of breast images represent effective tools in the reduction of morbidity and mortality associated with breast cancer, contributing to the early detection, assessment and diagnosis of breast cancer, image-guided biopsy and treatment planning and response monitoring. The analysis of breast images is still mostly done manually, but the use of computer-aided detection/diagnosis (CAD) systems as a second reader has been shown to help radiologists make final patient management decisions. The majority of breast image analysis CAD systems have been developed to work with several imaging modalities, such as: mammography, tomosynthesis, computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI). However, recent advances in the field are focused on the analysis of multimodal breast images, the use of deep learning methods, and the biomechanical modelling of the breast. This special session targets presentations on this topic, where we invited worldwide renowned speakers to present recent works addressing challenging problems in breast image analysis. This is an extremely relevant topic to ISBI given the relatively large number of recent publications in this particular field at all major medical image analysis conferences (ISBI, MICCAI, IPMI). Additionally, breast image analysis has been addressed by the field at least for the last two decades, making it one of the most studied problems in the field.

Other special sessions

Bio-inspired data mining and deep learning in biomedical image processing
Medical Imaging in Stroke
From Lab Technology to Clinical Applications: Latest Advances in Functional Near Infrared Spectroscopy (fNIRS)
Tele-Health: Assessing health with real-world constraints
Pediatric Neuroimaging