CCX-rayNet: A Class Conditioned Convolutional Neural Network For Biplanar X-Rays to CT Volume
Bibliographic record
Abstract
Despite the advancement of the deep neural network, the 3D CT reconstruction from its correspondence 2D X-ray is still a challenging task in computer vision. To tackle this issue here, we proposed a new class-conditioned network, namely CCX-rayNet, which is proficient in recapturing the shapes and textures with prior semantic information in the resulting CT volume. Firstly, we propose a Deep Feature Transform (DFT) module to modulate the 2D feature maps of semantic segmentation spatially by generating the affine transformation parameters. Secondly, by bridging 2D and 3D features (Depth-Aware Connection), we heighten the feature representation of the X-ray image. Particularly, we approximate a 3D attention mask to be employed on the enlarged 3D feature map, where the contextual association is emphasized. Furthermore, in the biplanar view model, we incorporate the Adaptive Feature Fusion (AFF) module to relieve the registration problem that occurs with unrestrained input data by using the similarity matrix. As far as we are aware, this is the first study to utilize prior semantic knowledge in the 3D CT reconstruction. Both qualitative and quantitative analyses manifest that our proposed CCX-rayNet outperforms the baseline method.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".