Guest Editorial Emerging Challenges for Deep Learning

The papers in this special section focus on the emerging challenges for deep learningin the biomedical industry. Due to the proliferation of biomedical imaging modalities such as Photoacoustic Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, Single Photon Emission Computed Tomography (SPECT), Magnetic Resonance (MR) Imaging, Ultrasound, Positron Emission Tomography (PET), Magnetic Particle Imaging, EE/MEG, Electron Tomography, and Atomic Force Microscopy, massive amounts of biomedical and health informatics data are being generated on a daily basis. How can we utilize such big data to build better health profiles and predictive models so that we can better diagnose and treat diseases and provide a better life for humans? In the past years, many successful learning methods such as deep learning were proposed to answer this crucial question, which has social, economic, as well as legal implications.