Convolutional Occupancy Networks for Medical Imaging with Applications to the KiTS23 Challenge
Bibliographic record
Abstract
We propose an application of occupancy networks for 3D medical image segmentation, demonstrating their effectiveness on the publicly available KiTS23 dataset. Unlike conventional CNN-based methods that operate in voxel space using encoder-decoder architectures, our approach represents anatomical structures as continuous decision boundaries within normalized coordinate space. This formulation enables fine-grained surface delineation and flexible inference resolution. Our architecture integrates a MedicalNet-pretrained ResNet encoder, a multi-scale Bi-directional Feature Pyramid Network (BiFPN) feature fusion backbone, and class-specific parallel prediction heads. To address the high anatomical variability and class imbalance in the dataset, we design a training strategy based on structured 3D patch sampling, coupled with a targeted refinement mechanism during inference that leverages coarse predictions to guide high-resolution queries for underrepresented classes. Extensive experiments show that our model achieves competitive performance on Dice and Surface Dice metrics compared to leaderboard methods. These results underscore the potential of continuous occupancy-based representations for high-fidelity medical segmentation.
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How this classification was reachedexpand
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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".