Pixel Distribution Learning for Vessel Segmentation under Multiple Scales
Bibliographic record
Abstract
In this work we try to address if there is a better way to classify two distributions, rather than using histograms; and answer if we can make a deep learning network learn and classify distributions automatically. These improvements can have wide ranging applications in computer vision and medical image processing. More specifically, we propose a new vessel segmentation method based on pixel distribution learning under multiple scales. In particular, a spatial distribution descriptor named Random Permutation of Spatial Pixels (RPoSP) is derived from vessel images and used as the input to a convolutional neural network for distribution learning. Based on our preliminary experiments we currently believe that a wide network, rather than a deep one, is better for distribution learning. There is only one convolutional layer, one rectified linear layer and one fully connected layer followed by a softmax loss in our network. Furthermore, in order to improve the accuracy of the proposed approach, the RPoSP features are captured at multiple scales and combined together to form the input of the network. Evaluations using standard benchmark datasets demonstrate that the proposed approach achieves promising results compared to the state-of-the-art.
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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 | 0.001 |
| 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 | 0.000 |
| 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 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".