sach3v/Domain_aware_dual_head_HEar

HeAR-TB Domain-Aware Dual Heads

Domain-Aware Adaptation of Google HeAR for TB Cough Screening

This model adapts Google's HeAR (Health Acoustic Representations) foundation model for tuberculosis cough screening using a novel domain-aware dual-head approach.

Model Description

• Base Model: Google HeAR (frozen)
• Method: Domain-Aware Dual Heads
  • One XGBoost head specialized on Passive (natural) coughs
  • One XGBoost head specialized on Forced (voluntary) coughs
  • Final patient score = average of both domain predictions (patient-level aggregation)
• Embedding Weighting: Embedding norm used as quality weight per cough
• Dataset: Nairobi TBscreen (Sharma et al., Science Advances 2024)
  • 27,343 clean coughs (Passive + Forced)

Performance (Patient-Level, Repeated 5-Fold CV)

Metric	Value
Patient AUC	0.7476 ± 0.0932
Sensitivity	~0.78–0.83 (tunable)
Accuracy	0.728 ± 0.060

Intended Use

• Research and demonstration of acoustic TB screening using foundation models
• Part of the MedGemma Impact Challenge (HAI-DEF model usage)
• Educational / exploratory use on cough audio

Limitations & Important Disclaimer

This is NOT a medical device or diagnostic tool.

• Trained on a small number of patients (123)
• Performance is research-level only
• Must not be used for clinical decision-making
• Requires proper clinical validation before any real-world use
• May contain biases from the training data (hospital population in Nairobi)

Always consult a qualified healthcare professional for medical advice.

CITE

@misc{hear-tb-2026, author = Sachiv.C, title = {HeAR-TB: Domain-Aware Dual Heads for Tuberculosis Cough Screening}, year = {2026}, dataset =https://zenodo.org/records/10431329, howpublished = (https://huggingface.co/sach3v/Domain_aware_dual_head_HEar), note = {Entry for MedGemma Impact Challenge} }

How to Use

import joblib
import numpy as np
import librosa
from huggingface_hub import hf_hub_download

# Download and load
package = joblib.load(hf_hub_download("sach3v/hear-tb-domain-aware-dualheads", "hear_tb_prize_domain_aware.joblib"))

model_p = package["model_p"]
scaler_p = package["scaler_p"]
model_f = package["model_f"]
scaler_f = package["scaler_f"]

def predict_cough(audio_path):
    y, sr = librosa.load(audio_path, sr=16000)
    # ... (pad to 2s and get HeAR embedding)
    emb = get_hear_embedding(y)   # your HeAR extraction function
    
    p_passive = model_p.predict_proba(scaler_p.transform(emb))[0,1]
    p_forced = model_f.predict_proba(scaler_f.transform(emb))[0,1]
    
    return (p_passive + p_forced) / 2.0