Function tissue unit segmentation based on UNext model
Bibliographic record
Abstract
Accurate segmentation for Functional Tissue Units (FTUs) is a challenging issue in past decades. In this study, a model using the dataset of tissue section images will be built to evaluate and mark FTUs across five human organs as clearly as possible. We have the Human Protein Atlas (HPA) as training data and the data from Human BioMolecular Atlas Program (HuBMAP) as testing data. To balance accuracy and inference speed, this study applied Unext, an efficient network based on Unet, as the basic model. We also aim to use some tricks to further improve the performance of the model. First, we used several image enhancement methods to diversify the input image. Second, several structures like Feature Pyramid Network (FPN) and the Atrous Spatial Pyramid Pooling are added to improve model performance and convergence speed. As a result, we successfully segment functional tissue units among images of different sizes. Our proposed model scored 0.56 out of 1.00 by the judge of the competition.
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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) | 0.000 | 0.006 |
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".