Ultrasound Breast Image Classification Through Domain Knowledge Integration Into Deep Neural Networks
Bibliographic record
Abstract
Current deep learning methods used for classifying ultrasound breast images have difficulty to learn and generalize with small training datasets containing images with tumors of varying size and shapes. A model that integrates domain knowledge into the classification model is proposed. The proposed model consists of two shallow CNN streams (image stream and mask stream), guide blocks, multiscale fusion block, and classification layers. The image stream extracts features from the ultrasound (US) image, while the mask stream extracts features from either the ground truth (GT) mask or the mask generated by either U-net or selective U-Net (SU-net). Guide blocks fuse the features provided by the mask stream into the image stream and by reweighting the features from the image stream, help the model focus on the tumor area along with the affected cells around them. The multiscale fusion block aggregates features from various levels of the image stream to address the various sizes of the tumors. Finally, classification layers provide the final decisions. The proposed model is trained using two approaches after augmenting the training set. The first approach trains the model using US images with an associated GT mask and then tests it using the US images with their predicted mask provided by either U-net or SU-net. The second approach is similar to the first one, except that the trained model is retrained using US images and the predicted mask. The proposed model outperforms 15 state-of- the-art methods, with about 7.5% increase in balanced accuracy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".