This report outlines the author's research activities at the Hasso Plattner Institute, focusing on deep learning applications in medical image analysis, particularly for brain disease diagnosis. The author discusses their PhD plan and introduces several novel, end-to-end trainable approaches for analyzing medical images. The report highlights three main sections: classification, localization, and segmentation of brain abnormalities. In the classification section, the author describes a deep architecture based on the VGG-16 network, which uses multi-channel convolution and L2 norm pooling layers to achieve high accuracy in classifying brain abnormalities. The detection and localization section details a method that leverages multi-modal 2D images and local and global contextual features to improve the accuracy of tumor detection. The semantic segmentation section presents a fast and accurate deep convolutional architecture for instance-level segmentation, achieving high accuracy in segmenting brain lesions. The report also includes a detailed description of the datasets used for evaluation, such as the IXI dataset for healthy brain images, the BRATS dataset for glioma tumors, the OASIS dataset for Alzheimer's disease, and the ISBI dataset for multiple sclerosis. The author concludes with a plan for future work, including further research on 3D semantic segmentation and the application of generative adversarial networks for image synthesis.This report outlines the author's research activities at the Hasso Plattner Institute, focusing on deep learning applications in medical image analysis, particularly for brain disease diagnosis. The author discusses their PhD plan and introduces several novel, end-to-end trainable approaches for analyzing medical images. The report highlights three main sections: classification, localization, and segmentation of brain abnormalities. In the classification section, the author describes a deep architecture based on the VGG-16 network, which uses multi-channel convolution and L2 norm pooling layers to achieve high accuracy in classifying brain abnormalities. The detection and localization section details a method that leverages multi-modal 2D images and local and global contextual features to improve the accuracy of tumor detection. The semantic segmentation section presents a fast and accurate deep convolutional architecture for instance-level segmentation, achieving high accuracy in segmenting brain lesions. The report also includes a detailed description of the datasets used for evaluation, such as the IXI dataset for healthy brain images, the BRATS dataset for glioma tumors, the OASIS dataset for Alzheimer's disease, and the ISBI dataset for multiple sclerosis. The author concludes with a plan for future work, including further research on 3D semantic segmentation and the application of generative adversarial networks for image synthesis.