metadata
license: mit
task_categories:
- visual-question-answering
tags:
- medical-imaging
- vision-language-models
- ct
- hallucination
- benchmark
pretty_name: Priors Over Pixels — Result Data
Priors Over Pixels — Result Data
Per-model result JSONs for the MICCAI 2026 SAFER workshop paper "Priors Over Pixels: Present-Bias in Organ-Presence Grounding for Medical VLMs."
These are the raw model verdicts behind every table in the paper — a POPE-style organ-presence probe on BTCV abdominal CT, testing whether medical VLMs ground their answers in pixels or recite anatomical priors.
- Code / reproduction: https://github.com/MR-Nazarov/priors-over-pixels
- Models probed: MedGemma-4B/27B, Gemma3-4B/27B (base ablation), Qwen2.5-VL-7B, LLaVA-Med
Files
| file | contents |
|---|---|
pope_results.json |
MedGemma-27B (main run) |
pope_results_medgemma4b.json |
MedGemma-4B |
pope_results_qwen.json |
Qwen2.5-VL-7B |
pope_results_llavamed.json |
LLaVA-Med |
gemma3_4b_pope.json / gemma3_27b_pope.json |
base-Gemma3 ablation |
pope_summary_allmodels.json, pope_abstention.json, adv_zdist_*.json, pope_noimg_summary.json |
derived summaries |
Schema
Each file is {"records": [...]}; a record is one (sample_id, organ, format)
verdict:
{
"sample_id": "img0023_z070_prior_consistent_pancreas",
"organ": "gallbladder",
"neg_strategy": "adversarial",
"ground_truth_present": false,
"parsed_present": true,
"parse_fail": false,
"format": "freetext",
"raw": "gallbladder, left kidney, pancreas"
}
Usage
hf download Lexer1/priors-over-pixels-data --repo-type dataset --local-dir .
# then run the analysis scripts from the code repo
License
MIT. Contains model outputs and derived statistics over public BTCV case identifiers — no patient data. BTCV itself is not redistributed here.