Coronary atherosclerosis is a leading cause of morbidity and mortality worldwide. It is often treated by placing stents in the coronary arteries. Inappropriately placed stents or malappositions can result in post-interventional complications. Intravascular Ultrasound (IVUS) imaging offers a potential solution by providing real-time endovascular guidance for stent placement. The signature of malapposition is very subtle and requires exploring second-order relationships between blood flow patterns, vessel walls, and stents. In this paper, we perform a comparative study of various deep learning methods and their feature extraction capabilities for building a malapposition detector. Our results in the study address the importance of incorporating domain knowledge in performance improvement while still indicating the limitations of current systems for achieving clinically ready performance.