• The proceeding is publicly available and can be accessed through the link here

• Best Paper Award

Continual Domain Incremental Learning for Chest X-ray Classification in Low-Resource Clinical Settings

Shikhar Srivastava (Mohamed Bin Zayed University of AI)*; Mohammad Yaqub (Mohamed Bin Zayed University of Artificial Intelligence); Karthik Nandakumar ( Mohamed Bin Zayed University of Artificial Intelligence); Zongyuan Ge (Monash); Dwarikanath Mahapatra (Inception Institute of Artificial Intelligence)

• Runners-up:

  1. Sickle Cell Disease Severity Prediction from Percoll Gradient Images using Graph Convolutional Networks

Ario Sadafi (Helmholtz Zentrum München)*; Asya Makhro (University of Zurich); Leonid Livshits (University of Zurich); Nassir Navab ("TU Munich, Germany"); Anna Bogdanova (University of Zurich); Shadi Albarqouni (Helmholtz AI | TU Munich); Carsten Marr (Helmholtz-muenchen)

• 2. Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student Learning

Başar Demir (Istanbul Technical University)*; Alaa Bessadok (University of Sousse, Tunisia); Islem Rekik (Istanbul Technical University)

• Decisions on the White Papers will be communicated to the authors on 30th July 2021.

• Reviews, Meta-Reviews, and Decision were communicated to the authors on 22nd July 2021

• Due to several requests, the deadline got extended once again to Thursday, July 1 at 11:59 Pacific Time

• NVIDIA is sponsoring an RTX 3090 for the best paper award

• Authors of selected best papers will be invited to submit extended versions to a special issue of MELBA, Machine Learning for Biomedical Imaging.

• Submission deadline: Sunday, June 20 Extended to Friday, June 25 Thursday, July 1 at 11:59 Pacific Time

• Notifications: Friday, July 16 --> Friday, July 23

• Camera-ready submission: Monday, July 26 --> Friday, July 30

• Workshop: Monday, September 27

As we witness a technological revolution that is spinning diverse research fields including healthcare at an unprecedented rate, we face bigger challenges ranging from the high cost of computational resources to the reproducible design of affordable and innovative solutions. While AI applications have been recently deployed in the healthcare system of high-income countries, its adoption in developing and emerging countries remains limited. Given the breadth of challenges faced particularly in the field of healthcare and medical data analysis, we present the first workshop aiming to i) raise awareness about the global challenges in healthcare, ii) strengthen the participation of underrepresented communities at MICCAI, and iii) build a community around Affordable AI and Healthcare in low resource settings. Our workshop stands out from other MICCAI workshops as it prioritizes and focuses on developed AI solutions and research suited to low infrastructure, point-of-care-testing, and edge devices. Examples include, but are not limited to AI deployments in conjunction with conventional X-rays, Ultrasound, microscopic imaging, retinal scans, fundus imaging, and skin lesions. Moreover, we encourage works that identify often neglected diseases prevalent in low resource countries and propose affordable AI solutions for such diseases using medical images. We are looking for contributions on (a) making AI affordable for healthcare, (b) making healthcare affordable with AI, or (c) pushing the frontiers of AI in Healthcare that enables (a) or (b).

There are formidable challenges that remain untackled in this field which are spanned by different questions: (1) How to promote knowledge sharing between AI institutions for developing countries?; (2) how to build robust and affordable solutions that can be used with limited technology and poor data and communication quality?; and (3) how can we propel the collection and analysis of data for underrepresented populations with limited quality and annotation? (4) how can we encourage computer scientists and clinicians to identify unmet clinical needs in neglected diseases that can benefit from affordable AI solutions? —just to raise a few.