Computer Aided Diagnosis (CAD) systems assist physicians in the interpretation of medical images. One of the most common application for CAD systems is in the field of mammography where they help in the detection and analysis of lesions e.g. masses, microcalcifications and tumors. X-ray mammography is the most used method for breast cancer screening and CAD systems for this modality are well established. CAD systems do also exist for other modalities like ultrasound and magnetic resonance imaging. A further modality is digital breast tomosynthesis (DBT) which uses image recordings from different angles to produce a 3D-representation of the breast. These 3D representations can overcome some shortcomings of conventional X-ray mammography like overlapping breast tissue due to the necessary breast compression during an examination. Using digital breast tomosynthesis has shown to increase accuracy and leading to a reduced false positive rate in breast cancer screening.
CAD systems are usually trained for a single modality and therefore often not applicable to other modalities. They rely upon state of the art machine learning approaches like deep learning. In general deep learning needs high amounts of data for training which for specific problems is often not available or hard to come by. Especially labelling and annotation of images is a labour-intensive and expensive task. Being able to come by the issue of limited modality usage and the problem of few data by combining different data sets would be beneficial for an automatic lesion detection method. However, combining data from different sources usually makes further model adaption necessary.
For this thesis two data sets of labeled mammography images are available. The first data set contains X-ray mammograms of 237 patients. The second consists of digital breast tomosynthesis images of 42 patients. The task of this thesis is to incorporate tomosynthesis images into a method for lesion detection in mammograms. This incorporation should improve the methods ability to detect lesions in mammograms and expand the method’s usability to a second modality.
This task can be considered as a problem of Domain Adaption  with two source and one target distribution. The source distributions are mammograms and tomosynthesis images whereas the target distribution are mammograms. One method of Domain Adaption appropriate for this task is adversarial-based Domain Adaptation with generative models. In the medical context Generative Adversarial Networks (GANs) have been applied for e.g. the translation of images between modalities, denoising of images and artifact correction . In this thesis GANs will be used to generate synthetic mammograms out of tomosynthesis data which will be used to enhance the data set used for the training of a deep convolutional neural network (CNN). The hypothesis is that using such an enhanced data set for model training leads to an improved performance in lesion detection compared to training with a smaller data set solely consisting of genuine mammograms.
The master thesis contains several milestones:
- Creation of a baseline deep CNN model trained and evaluated for lesion detection in mammograms.
- Development and optimization of a Cycle-GAN  to translate tomosynthesis images into mammograms.
- Evaluation of the classification performance for lesion detection of a deep CNN model trained on mammograms and Cycle-GAN generated mammograms.
- Application of the model obtained in 2 to verify the hypothesis on another publicly available mammogram data set.
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 Armanious K., Jiang C., Fischer M., Küstner T., Hepp T., Nikolaou K., Yang B. (2020). MedGAN: Medical image translation using GANs. Computerized Medical Imaging and Graphics,79, 1–16. https://doi.org/10.1016/j.compmedimag.2019.101684
 Zhu J. Y., Park T., Isola P. and Efros A. A. (2017). Unpaired Image-to-Image Translation Us-ing Cycle-Consistent Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision, 2017-October, 2242–2251. https://doi.org/10.1109/ICCV.2017.244