Medical imaging is increasingly gaining importance in medicine. Radiologists use images to detect diseases in an early stage (via screening), to diagnose patients with symptoms, and to monitor the effect of treatment. In pathology digitization of microscopy imaging is just starting, enabling pathologists to use computerized analysis of high-resolution gigapixel images to better diagnose disease in tissue samples. 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 intelligent image analysis systems that can aid clinicians with image interpretation and decisions. The goal of these systems is to reproduce visual skills of highly trained human observers and to provide quantitative analysis. For this purpose, machine learning is applied to develop a computer model that can be trained exploiting information from a large amounts of medical images.
 

In recent years, Deep Learning [LeCun et al., Nature, 2015] has emerged as the state-of-the-art approach for image analysis applications. While human readers still are superior in most applications, Convolutional Neural Networks 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. Finally, recent studies have shown that deep learning algorithms have reached and outperformed human professionals at diagnostic tasks like detection of skin cancer [Esteva et al., Nature, 2017], classification of diabetic retinopathy [Gulshan et al., JAMA, 2016] and detection of breat cancer metastasis in lymph nodes [Ehteshami et al., JAMA, 2017].

In the first part of the course, students will learn basic concepts of digital image processing, medical imaging, machine learning and deep learning through a series of weekly practical assignments. Lectures and assignments will cover the following topics: 

The second part of the course is dedicated ot the development of a CAD system based on deep learning, in the form of a final project, in which teams of students will apply the concepts and techniques learned in the first part of the course, and compete in ongoing grand challenges in medical imaging.


References
[LeCun et al., Nature, 2015] Yan LeCun, Yoshua Bengio,  Geoffrey Hinton, "Deep Learning", Nature, 521, 436–444, 2015
[Gulshan et al., JAMA, 2016] Varun Gulshan, Lily Peng, Marc Coram, et al., "Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs", JAMA. 2016;316(22):2402-2410
[Esteva et al., Nature, 2017] Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau, Sebastian Thrun, "Dermatologist-level classification of skin cancer with deep neural networks", Nature, 542, 115–11, 2017
[Ehteshami et al., JAMA, 2017] B. B. Ehteshami et al., "Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer", JAMA. 2017 Dec 12;318(22):2199-2210.