Improved Mammography Classification using Data Augmentation and Transfer LearningPETERKA, ANA (Avtor) Bosnić, Zoran (Mentor) Osipov, Evgeny (Komentor) Artificial IntelligenceMachine LearningDeep LearningConvolutional Neural NetworksGenerative Adversarial NetworksMammographyCancer Detection.Main goal of this thesis is to improve binary classification of mammograms, which could serve as a second opinion to the radiologists and give patients faster results. For this we studied the benefits of using data augmentation techniques and transfer learning. Training a deep convolutional neural network (DCNN) from scratch is difficult, because it requires large amounts of labeled training data. This is a big problem especially in the medical domain, since datasets are scarce and the data is often imbalanced - there is a higher prevalence of healthy results than pathological findings. This can result in overfitting the model. We try to mitigate this issue by generating novel data. We apply affine transformations to images as well as we generate new images that are produced by conditional infilling GAN. Using transfer learning improves classification and speeds the training process of prediction model. Our results show that we can relatively easy generate new and realistic looking data. With the help of transfer learning we can further improve the classification of benign and malignant mammograms.20222022-03-16 10:15:00Diplomsko delo/naloga135478sl