From Preprocessing to Fine-Tuning: Building the Foundation for Team Aventusoft
This was a pivotal week for our team! After spending time establishing our core data strategy, we successfully completed the preprocessing pipelines required to align single-lead data with the ECG-FM foundation model. We spent the week diving deep into the technical architecture of the fairseq framework, ensuring that our setup is robust and ready for the computational demands of model training.
These foundational steps have moved us from the planning phase into active experimentation, setting the stage for our first supervised fine-tuning runs using single-lead duplicated ECG signals.
Key Accomplishments This Week
- Multi-Channel Signal Synthesis and Validation: We completed preprocessing pipelines to extract Lead I ECG signals and duplicate them across 12 channels. This ensures full compatibility with the ECG-FM foundation model architecture while maintaining signal alignment and normalization.
- Architectural Mastery of fairseq-signals: The team conducted an in-depth study of the fairseq and fairseq-signals frameworks. We analyzed model architecture, training pipelines, and the specific mechanisms used to load and freeze pretrained checkpoints for downstream tasks.
- Fine-Tuning Infrastructure Ready: We reviewed prior ECG-FM training scripts and documentation to identify the necessary manifests, inputs, and hyperparameters. This preparation was essential to ensure our upcoming fine-tuning runs are efficient and stable.
- Data Integrity and Label Mapping: To ensure clinically meaningful results, we established a plan for patient-level training, validation, and test splits to avoid data leakage. We also began generating supervised diagnostic labels for our dataset using clinical metadata and established mappings.
Next Steps: Launching Experiments and Benchmarking
With the infrastructure finalized and the project remaining on schedule, next week is all about execution. We will launch the first fine-tuning experiments using the ECG-FM pretrained checkpoint with our single-lead duplicated inputs.
A major priority will be monitoring training stability and loss convergence to verify the integration of our new preprocessing pipeline. We will also begin comparing our fine-tuned results against simpler single-lead baseline models to contextualize performance gains. Finally, we will coordinate with our liaison engineers to confirm preferred evaluation metrics like AUROC and F1-score, ensuring our model meets the high standards required for clinical disease classification.
See you next week!