Learning from Few Subjects with Large Amounts of Voice ... · John V. Guttag Robert E. Hillman...
Transcript of Learning from Few Subjects with Large Amounts of Voice ... · John V. Guttag Robert E. Hillman...
-
Jose Javier Gonzalez Ortiz
Learning from Few Subjects with Large Amounts of Voice Monitoring Data
John V . Gut tagRober t E . H i l lman Daryush D . Mehta Ja r rad H . Van S tan Marzyeh Ghassemi
-
Jose Javier Gonzalez Ortiz
• Few subjects and large amounts of data→ Overfitting to subjects
• No obvious mapping from signal to features→ Feature engineering is labor intensive
• Subject-level labels→ In many cases, no good way of getting sample
specific annotations
1
Challenges of Many Medical Time Series
-
Jose Javier Gonzalez Ortiz
• Few subjects and large amounts of data→ Overfitting to subjects
• No obvious mapping from signal to features→ Feature engineering is labor intensive
• Subject-level labels→ In many cases, no good way of getting sample
specific annotations
2
Challenges of Many Medical Time Series
Unsupervised feature extraction
Multiple Instance Learning
-
Jose Javier Gonzalez Ortiz
128 x 64 128 x 64Conv + BatchNorm + ReLUPoolingDenseUpsamplingSigmoid
3
Learning Features
• Segment signal into windows• Compute time-frequency representation• Unsupervised feature extraction
-
Jose Javier Gonzalez Ortiz4
Ours
Raw Waveform Spectrogram Encoder
Logistic Regression
• % Positive
Prediction
Per Window Per Subject
Raw Waveform Spectrogram Encoder
Logistic Regression
• % Positive
Prediction
Per Window Per Subject
Classification Using Multiple Instance Learning
• Logistic regression on learned features with subject labels
• Aggregate prediction using % positive windows per subject
-
Jose Javier Gonzalez Ortiz5
Application: Voice Monitoring Data
• Voice disorders affect 7% of the US population
• Data collected through neck placed accelerometer
1 week = ~4 billion samples
~100
-
Jose Javier Gonzalez Ortiz
Previous work relied on expert designed features[1]
6
Results
AUC Accuracy
Expert LRTrain 0.70 ± 0.05 0.71 ± 0.04
Test 0.68 ± 0.05 0.69 ± 0.04
OursTrain 0.73 ± 0.06 0.72 ± 0.04
Test 0.69 ± 0.07 0.70 ± 0.05
[1] Marzyeh Ghassemi et al. Learning to detect vocal hyperfunction from ambulatory neck-surface acceleration features: initial results for vocal fold nodules. IEEE Trans. Biomed Engineering
Comparable performance without task-specific feature engineering!
-
Jose Javier Gonzalez Ortiz
• Our method learns features from large time series data
• Reduces the need for laborious task-specific feature engineering
• Applied to large voice monitoring dataset
• Comparable performance to previous work that relied on expert engineered features
7
Summary