AIIIMA 2022

 Artificial Intelligence over Infrared Images for Medical Applications [Hybrid Event]

 AIIMA 2022 Springer proceedings is available at https://link.springer.com/book/10.1007/978-3-031-19660-7 

MICCAI AIIIMA Workshop Description

There have been many advances in the use infrared imaging for medical applications in recent years. Infrared imaging comprises near-infrared, mid-wave infrared and long wave infrared spectrum. In the first ever AIIIMA workshop, more focus is given for long wave infrared imaging, in other words, thermal imaging. In the last two decades, infrared thermal imaging hardware devices have improved in thermal sensitivity by orders of magnitude (0.8 to 0.02 deg C) and recent research explorations have unearthed multiple clinical use cases and medical applications for thermal imaging. This non-contact, non-invasive radiation-free low-cost portable modality offers several advantages over other imaging modalities to patient monitoring applications in general. Furthermore, since thermal images have some unique imaging characteristics with inbuilt anonymity and opportunity to analyze in both thermal and imaging feature space, it has sparked considerable interest in several research communities. A simple pubmed search with the combination of words ‘Thermal imaging ’ and ‘Medical’ results in over 5000 articles just in the last decade. This surge of applications is 10 times higher when compared to the publications on thermal imaging during 2000-2010. We are also starting to see innovative startups creating breakthrough solutions leveraging this emerging trend of advanced medical infrared thermology and use of novel machine learning algorithms over the captured thermal images. We believe it is time to create a forum to discuss this specific sub-topic at MICCAI and promote this novel area of research among the research community that has the potential to hugely impact our society.

Some specific clinical applications of Infrared Imaging are seen in screening, diagnosis and treatment of cancer, such as breast cancer, skin cancer, oral cancer and others. Utility in evaluating skeletomuscular issues such as rheumatoid arthritis, vascular complications such as detecting early onset of diabetic foot and river blindness (parasites under the skin) or even respiratory abnormalities such as pneumonia and COVID-19.  The patient monitoring applications include treatment monitoring during neoadjuvant chemotherapy, acupuncture, cryotherapy and pain management.  A focused discussion on the topic of machine analysis of  medical Infrared Imaging can help create new radiomic biomarkers that can help in several such clinical use cases. 

 Call for Papers

The research topics include but are not limited to novel machine learning and thermal image analysis algorithms for :

Registration 

The workshop is a hybrid event. Please follow the below steps for Registration:

1. To register for AIIIMA workshop, please create a login account at https://conferences.miccai.org/2022/en/REGISTRATION.html

2. Please select Workshop/Tutorial/Challenge (Virtual - September 22).

3. You can choose to attend either in person or virtual.

4. You have an option to choose to attend only the workshop session on (Virtual - September 22).

  Submission Guidelines

Submitted manuscripts must be in pdf format following formats available at Lecture Notes in Computer Science . Manuscripts should be at most 8 pages (content) + 2 pages (references and acknowledgements)

All accepted papers will be published as part of the MICCAI Satellite Events joint LNCS proceedings to be published by Springer Nature.

How to Submit?

The manuscripts should be submitted through our CMT paper submission site 

Important Dates

Tentative Program Schedule


Organizing Team

Sponsors

Main Conference

Questions?

Contact aiiima.miccai@gmail.com for any queries about the MICCAI AIII Workshop

Reviewers

We thank the reviewers for reviewing the manuscripts submitted to AIIIMA

Volunteers: 

Ramanathan Anandaramakrishnan 

List of Open Source Thermal Datasets Available Online

*The above datasets are listed for experimentation purposes only and we do not endorse the validity or veracity of the above datasets.