A New Triplet Convolutional Neural Network for Classification of Lesions on Mammograms
Bibliographic record
Abstract
Mammography provides a useful tool for breast cancer detection.However, many doctors have difficulty in making the right decision based on mammograms.This paper aims to set up a deep learning (DL) architecture that can effectively differentiate between benign and malignant tumors.Specifically, a new triplet convolutional neural network (CNN) was established with three subnetworks, each of which contains a succession of layer blocks.Each block consists of two convolutional layers, a dropout layer and a max-pooling layer.During operation, the region of interest (ROI) extracted from the mammogram is imputed to the first subnetwork, and processed by the Canny filter.The filtered results become the input of the second subnetwork, while the third subnetwork takes the whole image as input.To verify the effectiveness of our architecture, a set of 500 images from 301 patients was extracted from the DDSM database and augmented to 4,000 images, and divided into a training set (80 %) and a testing set (20 %).The results show that our architecture achieved an accuracy of 93.13 %, a sensitivity of 96 % and a specificity of 90.25 %.This research provides a desirable way to identify breast cancer based on mammography.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".