# Investigating ECG Diagnosis with Ambiguous Labels using Partial Label Learning

arXiv: https://arxiv.org/abs/2512.11095 · HTML: https://mlsanity.com/paper/2512.11095/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 47 references that are in the map.
- First posted on arXiv 11 Dec 2025.

## Builds on (most cited first, 21 of 21)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Automatic diagnosis of the 12-lead ECG using a deep neural network](https://mlsanity.com/paper/1904.01949/) (2019)
- [Learning a Deep ConvNet for Multi-label Classification with Partial Labels](https://mlsanity.com/paper/1902.09720/) (2019)
- [Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition](https://mlsanity.com/paper/1908.07325/) (2019)
- [Progressive Identification of True Labels for Partial-Label Learning](https://mlsanity.com/paper/2002.08053/) (2020)
- [Leveraged Weighted Loss for Partial Label Learning](https://mlsanity.com/paper/2106.05731/) (2021)
- [Instance-Dependent Partial Label Learning](https://mlsanity.com/paper/2110.12911/) (2021)
- [Structured Semantic Transfer for Multi-Label Recognition with Partial Labels](https://mlsanity.com/paper/2112.10941/) (2021)
- [Heterogeneous Semantic Transfer for Multi-label Recognition with Partial Labels](https://mlsanity.com/paper/2205.11131/) (2022)
- [Learning in Imperfect Environment: Multi-Label Classification with Long-Tailed Distribution and Partial Labels](https://mlsanity.com/paper/2304.10539/) (2023)
- [Domain-incremental Cardiac Image Segmentation with Style-oriented Replay and Domain-sensitive Feature Whitening](https://mlsanity.com/paper/2211.04862/) (2022)
- [Binary Classification with Confidence Difference](https://mlsanity.com/paper/2310.05632/) (2023)
- [In-Distribution and Out-of-Distribution Self-supervised ECG Representation Learning for Arrhythmia Detection](https://mlsanity.com/paper/2304.06427/) (2023)
- [Learning from Partial Label Proportions for Whole Slide Image Segmentation](https://mlsanity.com/paper/2405.09041/) (2024)
- [On the Power of Deep but Naive Partial Label Learning](https://mlsanity.com/paper/2010.11600/) (2020)
- [Dynamic Prototype Rehearsal for Continual ECG Arrhythmia Detection](https://mlsanity.com/paper/2501.07555/) (2025)
- [Partial Label Learning for Emotion Recognition from EEG](https://mlsanity.com/paper/2302.13170/) (2023)
- [ECG Feature Importance Rankings: Cardiologists vs. Algorithms](https://mlsanity.com/paper/2304.02577/) (2023)
- [Uncertainty-Aware Multi-view Arrhythmia Classification from ECG](https://mlsanity.com/paper/2506.06342/) (2025)

## Abstract (arXiv)

Label ambiguity is an inherent and largely unaddressed challenge in real-world electrocardiogram (ECG) diagnosis, arising from overlapping conditions and diagnostic disagreements. However, current ECG models are trained assuming clean and non-ambiguous annotations, limiting both the development and meaningful evaluation of models under real-world conditions. Although Partial Label Learning (PLL) frameworks are designed to learn from ambiguous labels, their effectiveness in medical time-series domains, ECG in particular, remains largely underexplored. We present the first systematic study of PLL methods for ECG diagnosis under both real and controlled ambiguity. First, we adapt nine PLL algorithms to multi-label ECG diagnosis under label ambiguity, and perform detailed evaluations on real clinical settings with multi-annotator diagnostic disagreements. Next, to study PLL effects on ECG in more depth under controlled settings, we introduce a diverse set of clinically motivated synthetic label ambiguities. Our experiments demonstrate that PLL methods vary substantially in robustness across ambiguity types and levels. Moreover, we observe that PLL generally outperforms standard supervised training under label ambiguity, highlighting the value of such frameworks. Through extensive analysis, we identify key limitations of current PLL approaches for clinical settings and outline future directions for developing robust and clinically aligned ambiguity-aware learning frameworks for ECG diagnosis.