Convolutional networks for kidney segmentation in contrast-enhanced CT scans
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Bibliographic record
Abstract
Organ segmentation in medical imagery can be used to guide patient diagnosis, treatment and follow ups. In this paper, we present a fully automatic framework for kidney segmentation with convolutional networks (ConvNets) in contrast-enhanced computerised tomography (CT) scans. In our approach, a ConvNet is trained using a patch-wise approach to predict the class membership of the central voxel in 2D patches. The segmentation of the kidneys is then produced by densely running the ConvNet over each slice of a CT scan. Efficient predictions can be achieved by transforming fully connected layers into convolutional operations and by fragmenting the maxpooling layers to segment a whole CT scan volume in a few seconds. We report the segmentation performance of our framework on a highly variable data-set of 79 cases using a variety of evaluation metrics.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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