Data Augmentation Based on Inverse Transform Sampling for Improved Tissue Classification via Electrical Impedance Spectroscopy
Bibliographic record
Abstract
Ultrasound-guided needle biopsy often leads to uncertain results due to poor visualization of targets in the image. Biopsy needles equipped with electrical impedance spectroscopy (EIS) sensors can be used to identify the tissue at the needle tip and provide more accurate needle guidance and lesion targeting. A machine learning algorithm is often used to classify the tissue based on the measured EIS spectrum. However, training machine learning algorithms requires large amounts of data, which is rarely available in biomedical applications. A solution to increase the size of the dataset and improve the performance of the classifier is to create synthetic data that closely mimics the original measured data. This paper proposes inverse transform sampling as a data augmentation method for EIS to bolster training dataset size. It exploits the cumulative distribution functions of the target data and uses inverse sampling to generate new, synthetic data. The method is demonstrated using an EIS dataset composing 13 different ex-vivo tissue types. The method is then validated by comparing the performance of the synthetic data to the original data through the use of an artificial neural network (ANN) and a convolutional neural network (CNN). The classification results indicate that classification sensitivity, precision, and accuracy increase by at least 27.38, 24.86, and 19.41%, respectively, when the classifiers used a mix of original data and data augmented with the proposed method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".