This report outlines the research activities of the author at the Hasso Plattner Institute, focusing on deep learning applications for medical image analysis, particularly in brain disease diagnosis. The author proposes novel end-to-end trainable approaches for brain abnormality detection, recognition, and segmentation. The research explores various deep learning methods for brain disease diagnosis, including classification, localization, and segmentation. The author aims to develop an advanced medical application based on deep learning for diagnosis, detection, instance-level semantic segmentation, and image synthesis from MRI to CT/X-ray. The author's approach includes classification using a network based on Krizhevsky et al. (2012), detection and localization using multi-modal 2D images, and semantic segmentation using a Faster R-CNN architecture. The proposed methods are evaluated on benchmark datasets, showing improved performance in terms of accuracy and dice coefficient. The author also plans to explore generative adversarial networks for image generation and synthesis, aiming to enhance data augmentation and improve the efficiency of medical image analysis. The research uses five different brain datasets, including healthy brain images, high and low-grade glioma, Alzheimer's disease, and multiple sclerosis. The author's future work includes 3D semantic segmentation on brain lesions, data and model parallelism on GPUs, and extending results to other anatomical areas. The study highlights the potential of deep learning in improving medical image analysis, particularly in brain disease diagnosis, with applications in computer-aided diagnosis, image registration, and multi-modal image analysis.This report outlines the research activities of the author at the Hasso Plattner Institute, focusing on deep learning applications for medical image analysis, particularly in brain disease diagnosis. The author proposes novel end-to-end trainable approaches for brain abnormality detection, recognition, and segmentation. The research explores various deep learning methods for brain disease diagnosis, including classification, localization, and segmentation. The author aims to develop an advanced medical application based on deep learning for diagnosis, detection, instance-level semantic segmentation, and image synthesis from MRI to CT/X-ray. The author's approach includes classification using a network based on Krizhevsky et al. (2012), detection and localization using multi-modal 2D images, and semantic segmentation using a Faster R-CNN architecture. The proposed methods are evaluated on benchmark datasets, showing improved performance in terms of accuracy and dice coefficient. The author also plans to explore generative adversarial networks for image generation and synthesis, aiming to enhance data augmentation and improve the efficiency of medical image analysis. The research uses five different brain datasets, including healthy brain images, high and low-grade glioma, Alzheimer's disease, and multiple sclerosis. The author's future work includes 3D semantic segmentation on brain lesions, data and model parallelism on GPUs, and extending results to other anatomical areas. The study highlights the potential of deep learning in improving medical image analysis, particularly in brain disease diagnosis, with applications in computer-aided diagnosis, image registration, and multi-modal image analysis.