Mammogram Tumor Segmentation with Preserved Local Resolution: An Explainable AI System
Bibliographic record
Abstract
Medical image segmentation is a crucial component of computer-aided diagnosis (CAD) systems, as it aids in identifying important areas in medical images. In order to achieve optimal segmentation results, it is important to preserve the resolution of the input image. The dilated convolution module was introduced to maintain resolution across layers of a deep convolutional neural network by increasing the receptive field exponentially while keeping the parameters increase linearly. However, one drawback of using dilated convolution is that it can result in local spatial resolution loss by increasing the sparsity of the kernel in checkboard patterns. This work proposes a double-dilated convolution module to maintain local spatial resolution in medical image segmentation tasks while having a large receptive field. The module is applied to tumor segmentation in breast cancer mammograms using the state-of-art Deeplabv3+ network. The study also evaluates the developed models with the Gradient weighted Class Activation Map (Grad-CAM) and compares the performance of lesion segmentation networks on mammogram screenings from the INBreast dataset before and after using the proposed dilation module. The results show that the proposed module effectively improves the segmentation performance.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".