Deep Learning for Medical Image Analysis
It addresses medical image analysis for healthcare applications, but appears incremental as it builds on existing deep learning approaches without specifying major breakthroughs.
The paper proposes novel, end-to-end trainable deep learning methods for medical image analysis, specifically focusing on brain abnormality detection, recognition, and segmentation, but does not provide concrete results or numbers.
This report describes my research activities in the Hasso Plattner Institute and summarizes my Ph.D. plan and several novels, end-to-end trainable approaches for analyzing medical images using deep learning algorithm. In this report, as an example, we explore different novel methods based on deep learning for brain abnormality detection, recognition, and segmentation. This report prepared for the doctoral consortium in the AIME-2017 conference.