Blog post IJCAI-ECAI 2026

Author: blind-paper-review

_posts/2026-08-27-IJCAI-ECAI-Muskaan-Chopra.md

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+---
+layout: post
+title: "Presenting Our Work at IJCAI-ECAI 2026 in Bremen"
+date: 2026-08-27
+author: Muskaan Chopra
+categories: [News]
+description: false
+tldr: "I presented our work on abstention for safer diabetic retinopathy screening at IJCAI-ECAI 2026 in Bremen, with a week full of research discussions, new perspectives, and ideas for what to explore next."
+---
+
+In August, I had the opportunity to attend **IJCAI-ECAI 2026 in Bremen**, representing the **AML Lab, Lamarr Institute, and University of Bonn**.
+
+On Friday, August 21, I presented our paper, *Knowing When Not to Predict: Self-Supervised Learning and Abstention for Safer DR Screening*, in the **AI and Health Special Track**.
+
+![Muskaan Chopra presenting at IJCAI-ECAI 2026](/assets/blog/2026-08-27-IJCAI-Muskaan-01.jpeg)
+
+The work, co-authored with **Lorenz Sparrenberg, Jan H. Terheyden, and Rafet Sifa**, studies an important question for reliable medical AI: **when should a model choose not to make a prediction?**
+
+Using diabetic retinopathy screening as our application, we investigate how self-supervised pretraining influences not only conventional predictive performance, but also the model's behaviour when it is allowed to abstain on uncertain cases. Our results show that longer self-supervised pretraining does not necessarily lead to more reliable selective predictions. While conventional performance can saturate relatively early, selective performance can continue to vary across checkpoints.
+
+This highlights the importance of evaluating AI systems beyond accuracy alone, particularly in safety-critical settings where identifying uncertain cases and referring them for expert review can be just as important as making the correct prediction.
+
+![Muskaan Chopra presenting the poster at IJCAI-ECAI 2026](/assets/blog/2026-08-27-IJCAI-Muskaan-02.jpeg)
+
+Beyond presenting our work, the week gave me the chance to attend keynotes and technical sessions and to have many interesting conversations across different areas of AI, including **trustworthy AI, medical imaging, large language models, and machine translation**. These discussions also gave me several new ideas and research questions to take forward.
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+I am especially grateful to my co-authors for all the work that went into this project. Overall, IJCAI-ECAI 2026 was a valuable week of presenting, exchanging ideas, and connecting with researchers across the AI community.