mlsanityData to 14 Oct 2026

Approaching Low-Cost Cardiac Intelligence with Semi-Supervised Knowledge Distillation

arXiv 2511.02851 ↗First posted 29 Oct 2025eess.SP cs.AI cs.LGcited by 0

On the map: Knowledge distillation → Distillation for medical imaging

Abstract (arXiv)

Deploying advanced cardiac artificial intelligence for daily cardiac monitoring is hindered by its reliance on extensive medical data and high computational resources. Low-cost cardiac intelligence (LCCI) offers a promising alternative by using wearable device data, such as 1-lead electrocardiogram (ECG), but it suffers from a significant diagnostic performance gap compared to high-cost cardiac intelligence (HCCI). To bridge this gap, we propose LiteHeart, a semi-supervised knowledge distillation framework. LiteHeart introduces a region-aware distillation module to mimic how cardiologists focus on diagnostically relevant ECG regions and a cross-layer mutual information module to align the decision processes of LCCI and HCCI systems. Using a semi-supervised training strategy, LiteHeart further improves model robustness under limited supervision. Evaluated on five datasets covering over 38 cardiovascular diseases, LiteHeart substantially reduces the performance gap between LCCI and HCCI, outperforming existing methods by 4.27% to 7.10% in macro F1 score. These results demonstrate that LiteHeart significantly enhances the diagnostic capabilities of low-cost cardiac intelligence systems, paving the way for scalable, affordable, and accurate daily cardiac healthcare using wearable technologies.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Distillation for medical imaging, one of 12 directions in Knowledge distillation. Builds on 19 of its 62 references that are in the map. First posted on arXiv 29 Oct 2025.

Builds on

19 of 62 references.

References · 19

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  3. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  4. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,884
  5. 2018Learning deep representations by mutual information estimation and maximizationcited by 1,273
  6. 2024Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Predictioncited by 1,023
  7. 2019Relational Knowledge Distillationcited by 889
  8. 2016f-GAN: Training Generative Neural Samplers using Variational Divergence Minimizationcited by 506
  9. 2019Correlation Congruence for Knowledge Distillationcited by 285
  10. 2022Knowledge Distillation from A Stronger Teachercited by 208
  11. 2019Automatic diagnosis of the 12-lead ECG using a deep neural networkcited by 164
  12. 2020Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XLcited by 158
7 more
  1. 2021Towards Understanding Knowledge Distillationcited by 158
  2. 2025Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language Modelcited by 36
  3. 2024Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillationcited by 19
  4. 2024Multi-Channel Masked Autoencoder and Comprehensive Evaluations for Reconstructing 12-Lead ECG from Arbitrary Single-Lead ECGcited by 9
  5. 2023Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction:A Multi-Dataset Studycited by 6
  6. 2023MVKT-ECG: Efficient Single-lead ECG Classification on Multi-Label Arrhythmia by Multi-View Knowledge Transferringcited by 5
  7. 2024CE-SSL: Computation-Efficient Semi-Supervised Learning for ECG-based Cardiovascular Diseases Detectioncited by 2

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  1. 2026EVL-ECG: Efficient ECG Interpretation With Multi-Aspect Heterogeneous Knowledge Distillation
  2. 2022LRH-Net: A Multi-Level Knowledge Distillation Approach for Low-Resource Heart Network
  3. 2026ECG Foundation Models and Medical LLMs for Agentic Cardiovascular Intelligence at the Edge: A Review and Outlook
  4. 2025CardioPatternFormer: Pattern-Guided Attention for Interpretable ECG Classification with Transformer Architecture
  5. 2026LSTrans: Efficient Knowledge Transfer for Lightweight and Automated ECG Classification
  6. 2026ECG-Reasoning-Benchmark: A Benchmark for Evaluating Clinical Reasoning Capabilities in ECG Interpretation
  7. 2024ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text
  8. 2026ECGQuest: Benchmarking and Fine-Tuning Language Models for Electrocardiography