Biomarker-Based Pretraining for Chagas Disease Screening in Electrocardiograms
Not on the map. Why?
Abstract (arXiv)
Chagas disease screening via ECGs is limited by scarce and noisy labels in existing datasets. We propose a biomarker-based pretraining approach, where an ECG feature extractor is first trained to predict percentile-binned blood biomarkers from the MIMIC-IV-ECG dataset. The pretrained model is then fine-tuned on Brazilian datasets for Chagas detection. Our 5-model ensemble, developed by the Ahus AIM team, achieved a challenge score of 0.269 on the hidden test set, ranking 5th in Detection of Chagas Disease from the ECG: The George B. Moody PhysioNet Challenge 2025. Source code and the model are shared on GitHub: github.com/Ahus-AIM/physionet-challenge-2025
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 10 references that are in the map. First posted on arXiv 10 Apr 2026.
Builds on
4 of 10 references.
References · 4
- 2020Learning from Noisy Labels with Deep Neural Networks: A Surveycited by 580
- 2019InceptionTime: Finding AlexNet for Time Series Classificationcited by 386
- 2019Automatic diagnosis of the 12-lead ECG using a deep neural networkcited by 164
- 2025Detection of Chagas Disease from the ECG: The George B. Moody PhysioNet Challenge 2025cited by 2
Built on by
Not cited yet.