Deep Learning Applications in Medical Care (DLAMC 2019)

Deep Learning Applications in Medical Care (DLAMC 2019)


The development of intelligent medical data analysis systems has experienced a significant boost in recent years thanks to the emergence of a machine learning paradigm known as deep learning. Deep learning (DL) algorithms have enabled development of highly accurate systems (with performance comparable to that of human experts, in some cases) and have become a standard choice for analyzing medical data, especially medical images, video, and electronic health records. Dozens of commercial applications using deep learning to analyze, classify, segment and measure data from different modalities of sensors and medical images are currently available. Deep learning methods applied on electronic health records are contributing to understand the evolution of chronic diseases and predicting the risk of developing those diseases. Researchers in industry, hospitals, and academia have published hundreds of scientific contributions in this area during the last year alone.


The “Deep Learning Applications in Medical Care” special track provides a forum for the discussion of the impact of deep learning on medical sensor/image/video data and electronic health record analysis and a focused venue for sharing novel scientific contributions in the area of deep learning.


The topics of interest include, but are not limited to:

  • Novel approaches for medical sensor/image data analysis, event detection, segmentation, and abnormality detection, object/lesion classification, organ/region/landmark localization, object/lesion detection, organ/substructure segmentation, lesion segmentation, and medical image registration using DL.
  • DL for electronic health records analysis.
  • Content-Based Image Retrieval (CBIR) of medical images using DL.
  • Medical sensor/image data understanding using DL.
  • Medical sensor/image data visualisation.
  • Sensor/image data generation and preprocessing methods using unsupervised DL like GANs,  autoencoders, etc.
  • Multimodal analysis and fusion using DL.
  • Applications of DL in different fields of medicine such as psychology.
  • Human behavior modelling using DL for mental healthcare applications.
  • Organ-specific (brain, eye, breast, heart, skin, lungs, abdomen, etc.), modality-specific (MRI, X-rays, PET, CT, color fundus images, etc.) and disease-specific image analysis using DL.
  • Applications of DL for digital pathology and microscopy.


  • Prospective authors are invited to submit papers in any of the topics listed above.
  • Instructions for preparing the manuscript (in Word and Latex formats) are available at: Paper Templates
  • Please also check the Guidelines.
  • Papers must be submitted electronically via the web-based submission system using the appropriated button on this page.


  • Enrique Garcia Ceja (University of Oslo, Norway)
  • Michael Riegler (SimulaMet & University of Oslo, Norway)
  • Pål Halvorsen (SimulaMet, Norway)
  • Venet Osmani (Fondazione Bruno Kessler, Italy)
  • Hugo Lewi Hammer, (Oslo Metropolitian University, Norway)
  • Klaus Schoeffmann, (Klagenfurt University, Austria)
  • Dag Johansen, (The Arctic University of Norway, Norway)
  • Svein Arne Pettersen (The Arctic University of Norway, Norway)


  • TBA