Breast Ultrasound Image Segmentation Model Based Residual Encoder
Bibliographic record
Abstract
Artificial intelligence algorithms show promise for various medical imaging applications. Modern computer-aided diagnosis (CAD) systems can potentially be used for the early diagnosis of breast tumors, a leading cause of death in women worldwide. Deep learning algorithms have been applied to readily available breast ultrasound (BUS) images and provided good segmentation and classification performance. Nonetheless, this task remains challenging because US images are very noisy with a class imbalanced data distribution and inhomogenous intensity. To address these issues, we proposed a new variant of the U-Net architecture, which is commonly used for medical image segmentation. We also increased the network depth by using residual blocks (RBs). To resolve the issue of the vanishing gradient while downsampling the features, we expanded the network width by adapting a convolution path instead of a concatenation path in the original U-net. Our proposed model includes a preprocessing stage, feature extraction based on RBs, convolution-concatenation path, and a simple decoder to reconstruct the extracted features. Our proposed model showed better performance than basic U-Net and most recent models like DAL, SK-U-Net, Mobile-U-Net, and Efficient-U-Net. In particular, our model achieved a Dice coefficient and IOU of 91.5% and 84.6% on BUSIS, respectively.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".