Course is confirmed
Medical imaging is increasingly gaining importance in medicine. Radiologists use imaging to detect diseases in an early stage, diagnose patients with symptoms, and monitor the effect of treatment. In pathology, digitization of microscopy imaging enables pathologists to use computerized analysis of high-resolution gigapixel images to diagnose disease in tissue samples better. However, as the complexity of imaging (3D/4D) and the amount of data increases, the interpretation of images by humans becomes problematic. Therefore, there is a growing need for artificial intelligence systems to aid clinicians with image interpretation and clinical decision-making. The goal of these systems is to reproduce the visual skills of highly trained human observers or to provide quantitative analysis.
Deep Learning has demonstrated promising performance in image analysis in the last decade. These deep-learning-based artificial intelligence algorithms have been successfully applied to medical imaging problems like automated reading of mammograms for breast cancer detection, automatic detection of pulmonary nodules for lung cancer screening, detection of breast and prostate cancer in histopathology images, and segmentation of white matter lesions in brain magnetic resonance, amongst others, de facto gradually bridging the gap between humans and computers. Recent studies have shown that deep learning algorithms have reached and outperformed human professionals at these diagnostic tasks.
Learning objectives
- Learn how basic methods for image processing work
- Learn about important applications in the field of medical image analysis
- Understand the increasing role of machine learning and deep learning in medical imaging
- Design, implement, and evaluate a medical image analysis system for a clinical application
- Learn how to design studies to evaluate medical image analysis systems
