Candidate Label Reconstruction with Cardinality Preservation for Multi-label ECG

ECG recordings often contain multiple co-occurring diagnoses, while clinical annotations may also be ambiguous or incomplete. Partial Label Learning (PLL) provides a natural framework for this setting by learning from candidate label sets that contain the underlying true labels. However, existing PLL disambiguation strategies are not well-suited to multi-label ECG diagnosis for two reasons: uniform disambiguation can unnecessarily alter reliable candidate sets, while confidence-based approaches may suppress valid but less confident co-occurring diagnoses. We propose a novel disambiguation method, ReCal, which learns sample-specific reliability to determine how strongly each candidate set should be disambiguated and estimates label cardinality to preserve multiple valid diagnoses. Using several controlled ambiguity settings on PTB-XL, Chapman, and AMIGOS datasets, ReCal consistently improves both label disambiguation and downstream classification over existing methods.