Medical Imaging meets NIPS

Medical imaging and radiology are facing a major crisis with an ever-increasing complexity and volume of data and immense economic pressure. With the current advances in imaging technologies and their widespread use, interpretation of medical images pushes human abilities to the limit with the risk of missing critical patterns of disease. Machine learning has emerged as a key technology for developing novel tools in computer aided diagnosis, therapy and intervention. Still, progress is slow compared to other fields of visual recognition, which is mainly due to the domain complexity and constraints in clinical applications, i.e. robustness, high accuracy and reliability.

“Medical Imaging meets NIPS” aims to bring researchers together from the medical imaging and machine learning communities to discuss the major challenges in the field and opportunities for research and novel applications. The proposed event will be the continuation of a successful workshop organized in NIPS 2017 (https://sites.google.com/view/med-nips-2017). It will feature a series of invited speakers from academia, medical sciences and industry to give an overview of recent technological advances and remaining major challenges.

Different from last year and based on feedback from participants, we propose to implement two novelties.
1. The workshop will accept paper submissions and have oral presentations with a format that aims to foster in depth discussions of a few selected articles. We plan to implement a Program Committee who will be responsible for reviewing articles and initiating discussions. The abstract track organized last year has brought a significant number of submission and has clearly demonstrated an appetite for more.
2. Along the workshop, we will host a challenge on outlier detection in brain Magnetic Resonance Imaging (MRI), which is one of the main applications of advanced unsupervised learning algorithms and generative models in medical imaging. The challenge will highlight a problem where the machine learning community can have a huge impact. To facilitate the challenge and potential further research, we provide necessary pre-processed datasets to simplify the use of medical imaging data and lower data-related entry barrier. Data collection for this challenge is finalized and ethical approval for data sharing is in place. We plan to open the challenge as soon as acceptance of the workshop is confirmed.