Index
Automatic Rotation of Spinal X-Ray Images
Guidance in orthopedic and trauma surgery is increasingly relying on intraoperative fluoroscopy with a mobile C-arm. Mobile fluoroscopy is also used to assess the success of fracture reduction, implant position, and overall outcome [1]. This reduces the number of necessary revision surgeries [2]. Accurate standardized image rotation is essential to improve reading performance and interpretation. While alignment of a patient with the imaging system is not always achievable, the images must be rotated manually by radiographers [3].
As the interaction of the user with the imaging system should be minimized, the goal of this thesis is to develop an automatic procedure that determines the orientation of the acquired images and rotates them to a standard position to be viewed by radiologists. In this work, the focus is on the regression of the image rotation of anterior-posterior (AP) and lateral radiographs of the spine since these are the most frequently acquired and most relevant for spine procedures.
[1] Lisa Kausch, Sarina Thomas, Holger Kunze, Maxim Privalov, Sven Vetter, Jochen Franke, Andreas H.
Mahnken, Lena Maier-Hein, and Klaus Maier-Hein. Toward automatic c-arm positioning for standard
projections in orthopedic surgery. Int. J. Comput. Assist. Radiol. Surg., 15(7):1095–1105, Jul 2020.
[2] Celia Mart´ın Vicario, Florian Kordon, Felix Denzinger, MarkusWeiten, Sarina Thomas, Lisa Kausch, Jochen
Franke, Holger Keil, Andreas Maier, and Holger Kunze. Automatic plane adjustment of orthopedic intraoperative
flat panel detector ct-volumes. In Proc. MICCAI, Part II, volume 12262, pages 486–495, 2020.
[3] Ivo M. Baltruschat, Axel Saalbach, Mattias P. Heinrich, Hannes Nickisch, and Sascha Jockel. Orientation
regression in hand radiographs: a transfer learning approach. In Proc. SPIE Medical Imaging, volume 10574,
pages 473 – 480, 2018.
Guided Attention Mechanism for Weakly-Supervised Breast Calcification Analysis
Thesis_Proposal_Akshat_SubmittedAutomatic identification of unremarkable Medical Images
Human interpretable Writer Retrieval and Verification
PowerPoint Presentation describer. Machine learning methods to automatically generate business captions from graphics
Detection of localized necking in Hydraulic Bulge Tests using Deep Learning Methods
Reinforcement Learning in Optimum Order Execution
Empathetic Deep Learning to the Rescue: Speech Emotion Recognition from Adults to Children
Emotional states are strong influential factors of humans’ choices, activities, and desires. They can be evaluated from face, self-observing reports and, what this thesis focuses on, speech. While there is some research done in speech emotion recognition it has less exploitation of deep learning approaches due to the field’s recentness and recent improvements in computational and optimizational approaches. In addition, the complicatedness of collecting improvised data, not from professional adult actors remains present in the state-of-the-art literature. Thus, the goal of this thesis is to explore the area of speech emotion recognition in children by testing the predominant approaches of neural networks with temporal prosody as well as abruptly expanding Transformers methods. We investigate the potential of transfer knowledge applied from adults’ to children’s data as the mechanism of dealing with lacking data. From the outcomes, we observe the improvement in the opportunities of transfer knowledge when gender and cultural aspects are included into the classification of emotions. Emotionally intelligent systems built based on the experiments described in the thesis can benefit the fields of remote monitoring or telemedicine for psychologists and pediatrists, teaching emotional intelligence for autistic children, and improving children’s health diagnostics and scanning procedures.