MEDIGAN MODEL UPLOAD: 00023_PIX2PIXHD_BREAST_DCEMRI
Bibliographic record
Abstract
Model ID: 00023_PIX2PIXHD_BREAST_DCEMRI. Uploaded via: API Tags: ['dce-mri', 'postcontrast', 'synthesis', 'breast', 'mri', 'treatment', 'i2i', 'pix2pixHD', 'SPIE'] Usage: This GAN is used as part of the medigan library. This GANs metadata is therefore stored in and retrieved from medigan's config file. medigan is an open-source Python library on Github that allows developers and researchers to easily add synthetic imaging data into their model training pipelines. medigan is documented here and can be used via pip install: pip install medigan To run this model in medigan, use the following commands. from medigan import Generators generators = Generators() generators.generate(model_id='00023_PIX2PIXHD_BREAST_DCEMRI',num_samples=10) Description from model config:: {"title": "Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation", "provided_date": "11.2023", "trained_date": "2023", "provided_after_epoch": 30, "version": "1.0", "publication": "https://doi.org/10.48550/arXiv.2311.10879", "doi": ["https://doi.org/10.48550/arXiv.2311.10879"], "inputs": ["pre-contrast t1-weighted breast mri"], "comment": "2d breast mri slice by slice generation of postcontrast data"}
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.264 | 0.196 |
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 source (direct Gemma or distilled Codex), 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".