Knowledge Distillation from General Multi-Objective ECG Classification Model for Chagas Disease Detection in 12-Lead ECGs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F25%3A00648258" target="_blank" >RIV/68081731:_____/25:00648258 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.cinc.org/archives/2025/pdf/CinC2025-132.pdf" target="_blank" >https://www.cinc.org/archives/2025/pdf/CinC2025-132.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.22489/CinC.2025.132" target="_blank" >10.22489/CinC.2025.132</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Knowledge Distillation from General Multi-Objective ECG Classification Model for Chagas Disease Detection in 12-Lead ECGs

Popis výsledku v původním jazyce

We present our solution (team ISIBrno-AIMT) for the 2025 George B. Moody PhysioNet Challenge, which utilizes a teacher-student model architecture. A key component of our approach is a generalized multi-objective teacher model based on the U-Net architecture, which was pre-trained on publicly available 12-lead ECG databases (containing over 1 million ECG recordings) for both the segmentation task and the classification task of 28 cardiac pathologies. The student models were specifically trained to distill knowledge from the teacher’s classification outputs, as well as to predict Chagas disease based on public challenge datasets containing Chagas disease labels. To improve the reliability of our predictions, we employed an ensemble of five student models, averaging their outputs during the inference stage. Our model achieved 4th place in the challenge, with an overall challenge score of 0.271.

Název v anglickém jazyce

Knowledge Distillation from General Multi-Objective ECG Classification Model for Chagas Disease Detection in 12-Lead ECGs

Popis výsledku anglicky

We present our solution (team ISIBrno-AIMT) for the 2025 George B. Moody PhysioNet Challenge, which utilizes a teacher-student model architecture. A key component of our approach is a generalized multi-objective teacher model based on the U-Net architecture, which was pre-trained on publicly available 12-lead ECG databases (containing over 1 million ECG recordings) for both the segmentation task and the classification task of 28 cardiac pathologies. The student models were specifically trained to distill knowledge from the teacher’s classification outputs, as well as to predict Chagas disease based on public challenge datasets containing Chagas disease labels. To improve the reliability of our predictions, we employed an ensemble of five student models, averaging their outputs during the inference stage. Our model achieved 4th place in the challenge, with an overall challenge score of 0.271.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20601 - Medical engineering

Projekt

<a href="/cs/project/FW06010766" target="_blank" >FW06010766: Distanční terapie u pacientů se srdečním selháním pomocí metod umělé inteligence s fúzí multimodálních vstupů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Computing in Cardiology 2025 (CinC 2025)

ISBN

—

ISSN

2325-887X

e-ISSN

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Počet stran výsledku

4

Strana od-do

132

Název nakladatele

Computing in Cardiology

Místo vydání

Neuveden

Místo konání akce

Sao Paulo

Datum konání akce

14. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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