A Multi-Scale Channel Attention Network for Prostate Segmentation
Why this work is in the frame
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Bibliographic record
Abstract
Prostate cancer is one of the most common malignant tumors in men. Magnetic resonance imaging (MRI) has evolved to an important tool for the diagnosis of prostate cancer. Targeted biopsy is required for accurate diagnosis. This often requires MRI-ultrasound (MRI-US) fusion, as the biopsy is usually performed using transrectal ultrasound. Accurate prostate segmentation on MRI is essential for MRI-US fusion biopsy. However, the variation in prostate shape, appearance, and size makes the automatic segmentation challenging, given the limit of the annotated data. In this brief, we propose a method using multi-scale and Channel-wise Self-Attention (CSA) to re-calibrate the feature maps from multiple layers. By embedding the multi-scale CSA on the skip-connection in a UNet structure, called as UCAnet, we show the consistent improvement of the prostate segmentation in Dice, IoU and ASSD. For comparison, we also investigate the single-scale CSA in the networks, and incorporate the vision transformer to test if a transformer would boost the performance. Experiments on a public dataset with 204 prostate MRI scans show that UCAnet achieves the best performance and outperforms the state-of-the-art methods for prostate segmentation such as ENet, UNet, USE-Net and TransUNet.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it