Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 172 tok/s
Gemini 2.5 Pro 48 tok/s Pro
GPT-5 Medium 30 tok/s Pro
GPT-5 High 29 tok/s Pro
GPT-4o 103 tok/s Pro
Kimi K2 199 tok/s Pro
GPT OSS 120B 464 tok/s Pro
Claude Sonnet 4.5 36 tok/s Pro
2000 character limit reached

Beyond the Leaderboard: Insight and Deployment Challenges to Address Research Problems (1811.03014v1)

Published 26 Oct 2018 in cs.CY, cs.CV, cs.LG, and stat.AP

Abstract: In the medical image analysis field, organizing challenges with associated workshops at international conferences began in 2007 and has grown to include over 150 challenges. Several of these challenges have had a major impact in the field. However, whereas well-designed challenges have the potential to unite and focus the field on creating solutions to important problems, poorly designed and documented challenges can equally impede a field and lead to pursuing incremental improvements in metric scores with no theoretic or clinical significance. This is supported by a critical assessment of challenges at the international MICCAI conference. In this assessment the main observation was that small changes to the underlying challenge data can drastically change the ranking order on the leaderboard. Related to this is the practice of leaderboard climbing, which is characterized by participants focusing on incrementally improving metric results rather than advancing science or solving the driving problem of a challenge. In this abstract we look beyond the leaderboard of a challenge and instead look at the conclusions that can be drawn from a challenge with respect to the research problem that it is addressing. Research study design is well described in other research areas and can be translated to challenge design when viewing challenges as research studies on algorithm performance that address a research problem. Based on the two main types of scientific research study design, we propose two main challenge types, which we think would benefit other research areas as well: 1) an insight challenge that is based on a qualitative study design and 2) a deployment challenge that is based on a quantitative study design. In addition we briefly touch upon related considerations with respect to statistical significance versus practical significance, generalizability and data saturation.

Citations (1)

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.