To combat the damaging effects of climate change, conserving energy is imperative. However, the increasing use of computational resources for medical image processing remains a concerning trend. That’s where our international challenge comes in: We aim to cultivate and evaluate innovative solutions that prioritize energy-efficiency in the development and deployment of medical image processing algorithms, ultimately resulting in more sustainable and superior solutions.
In addition to its potential benefits for climate change, the solutions developed in this challenge may be useful in two important ways: (1) They will enable the use of state of the art medical image analysis methods in situations where energy is scarce or expensive such as in developing countries and on battery-operated/handheld devices. (2) They will promote better training procedures to replace the current common practice that involves extensive hyper-parameter search and trial-and-error.
This challenge centers on two vital tasks in medical image analysis: segmentation and classification. The goal is to identify the methods that not only achieve good task performance, but also use as little energy as possible during model training and inference. We believe that the participants’ discoveries will set a new standard for energy efficiency and inspire further advances across various domains.
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The challenge is currently open.
In the first phase of the challenge, we will accept initial submissions that will not be ranked. We will allow each participating team three optional initial submissions. The dates for these test runs will be communicated here as soon as they are fixed.
The submission date for the final method is the 17th of September. Late submissions might be possible, but we cannot guarantee the execution of the submission.