🛰️ Daily AI Frontier
‹ back to 2026-08-07

ECG-LENS: Lead-Aware Clinical Context Enriched ECG Report Generation and Evaluation

Research Medical/Healthcare AI

Ranking

Overall 64
Content 75
Popularity 39

Observed public metrics from 1 member.

Representative image for ECG-LENS: Lead-Aware Clinical Context Enriched ECG Report Generation and Evaluation

Merged summary

TL;DR - ECG-LENS is an end-to-end framework that turns multi-lead ECG signals into clinical-grade text reports, paired with a new ECG-specific evaluation metric. It matters because most prior work stops at classification, leaving generated reports too weak for real clinical use.

  • Architecture combines lead-wise encoders (preserving localized waveform morphology) with a global encoder for inter-lead dependencies; fused signal representations plus clinically enriched textual prompts condition a GPT-2 decoder.
  • Adds an ECG-specific report-preprocessing strategy to steer the model toward clinically meaningful findings rather than boilerplate text.
  • Proposes F1-ECGBERT, a BERT-based metric scoring agreement between diagnostic labels extracted from generated vs. reference reports, addressing the known bias of lexical metrics like BLEU/ROUGE.
  • Evaluated in-domain on PTB-XL and cross-domain on MIMIC-IV-ECG, reporting absolute gains of 4.0% METEOR, 6.3% ROUGE-L, and 11.5% F1-ECGBERT over the strongest baselines.

Sources (1)

ECG-LENS: Lead-Aware Clinical Context Enriched ECG Report Generation and Evaluation

arXiv cs.AI Akanta Das, Tasinul Islam Ahon, Ahmed Mahir Sultan Rumi, Md Mahbubur Rahman, Tausif Amim Shadly, Tanzima Hashem 2026-08-06 arXiv:2608.05893
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-10 02:42:49.018944 UTC

TL;DR - ECG-LENS is an end-to-end framework that turns multi-lead ECG signals into clinical-grade text reports, paired with a new ECG-specific evaluation metric. It matters because most prior work stops at classification, leaving generated reports too weak for real clinical use.

  • Architecture combines lead-wise encoders (preserving localized waveform morphology) with a global encoder for inter-lead dependencies; fused signal representations plus clinically enriched textual prompts condition a GPT-2 decoder.
  • Adds an ECG-specific report-preprocessing strategy to steer the model toward clinically meaningful findings rather than boilerplate text.
  • Proposes F1-ECGBERT, a BERT-based metric scoring agreement between diagnostic labels extracted from generated vs. reference reports, addressing the known bias of lexical metrics like BLEU/ROUGE.
  • Evaluated in-domain on PTB-XL and cross-domain on MIMIC-IV-ECG, reporting absolute gains of 4.0% METEOR, 6.3% ROUGE-L, and 11.5% F1-ECGBERT over the strongest baselines.
item →