Datasets:

Lexer1
/

priors-over-pixels-data

Tasks:

Visual Question Answering

Tags:

medical-imaging

vision-language-models

hallucination

benchmark

License:

Dataset card Files Files and versions

xet

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train

The dataset viewer is not available for this split.

Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
per_bin_specificity: struct<0-5: struct<freetext: struct<spec: double, ci: list<item: double>, n: int64>, verdict_first:  (... 1026 chars omitted)
  child 0, 0-5: struct<freetext: struct<spec: double, ci: list<item: double>, n: int64>, verdict_first: struct<spec: (... 116 chars omitted)
      child 0, freetext: struct<spec: double, ci: list<item: double>, n: int64>
          child 0, spec: double
          child 1, ci: list<item: double>
              child 0, item: double
          child 2, n: int64
      child 1, verdict_first: struct<spec: double, ci: list<item: double>, n: int64>
          child 0, spec: double
          child 1, ci: list<item: double>
              child 0, item: double
          child 2, n: int64
      child 2, reasoning_first: struct<spec: double, ci: list<item: double>, n: int64>
          child 0, spec: double
          child 1, ci: list<item: double>
              child 0, item: double
          child 2, n: int64
  child 1, 5-10: struct<freetext: struct<spec: double, ci: list<item: double>, n: int64>, verdict_first: struct<spec: (... 116 chars omitted)
      child 0, freetext: struct<spec: double, ci: list<item: double>, n: int64>
          child 0, spec: double
          child 1, ci: list<item: double>
              child 0, item: double
          child 2, n: int64
      child 1, verdict_first: struct<spec: double, ci: list<item: double>, n: int64>
          child 0, spec: double
          child 1, ci: list<item: double>
              child 0, item
...
pec: double
  child 3, far_ge40_spec: double
llavamed|verdict_first: struct<slope_logodds_per_mm: double, n: int64, near_le5_spec: double, far_ge40_spec: double>
  child 0, slope_logodds_per_mm: double
  child 1, n: int64
  child 2, near_le5_spec: double
  child 3, far_ge40_spec: double
medgemma27b|freetext: struct<slope_logodds_per_mm: double, n: int64, near_le5_spec: double, far_ge40_spec: double>
  child 0, slope_logodds_per_mm: double
  child 1, n: int64
  child 2, near_le5_spec: double
  child 3, far_ge40_spec: double
qwen|verdict_first: struct<slope_logodds_per_mm: double, n: int64, near_le5_spec: double, far_ge40_spec: double>
  child 0, slope_logodds_per_mm: double
  child 1, n: int64
  child 2, near_le5_spec: double
  child 3, far_ge40_spec: double
medgemma4b|freetext: struct<slope_logodds_per_mm: double, n: int64, near_le5_spec: double, far_ge40_spec: double>
  child 0, slope_logodds_per_mm: double
  child 1, n: int64
  child 2, near_le5_spec: double
  child 3, far_ge40_spec: double
medgemma27b|verdict_first: struct<slope_logodds_per_mm: double, n: int64, near_le5_spec: double, far_ge40_spec: double>
  child 0, slope_logodds_per_mm: double
  child 1, n: int64
  child 2, near_le5_spec: double
  child 3, far_ge40_spec: double
medgemma4b|reasoning_first: struct<slope_logodds_per_mm: double, n: int64, near_le5_spec: double, far_ge40_spec: double>
  child 0, slope_logodds_per_mm: double
  child 1, n: int64
  child 2, near_le5_spec: double
  child 3, far_ge40_spec: double
to
{'medgemma4b|freetext': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}, 'medgemma4b|verdict_first': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}, 'medgemma4b|reasoning_first': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}, 'medgemma27b|freetext': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}, 'medgemma27b|verdict_first': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}, 'medgemma27b|reasoning_first': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}, 'llavamed|freetext': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}, 'llavamed|verdict_first': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}, 'llavamed|reasoning_first': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}, 'qwen|freetext': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}, 'qwen|verdict_first': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}, 'qwen|reasoning_first': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              per_bin_specificity: struct<0-5: struct<freetext: struct<spec: double, ci: list<item: double>, n: int64>, verdict_first:  (... 1026 chars omitted)
                child 0, 0-5: struct<freetext: struct<spec: double, ci: list<item: double>, n: int64>, verdict_first: struct<spec: (... 116 chars omitted)
                    child 0, freetext: struct<spec: double, ci: list<item: double>, n: int64>
                        child 0, spec: double
                        child 1, ci: list<item: double>
                            child 0, item: double
                        child 2, n: int64
                    child 1, verdict_first: struct<spec: double, ci: list<item: double>, n: int64>
                        child 0, spec: double
                        child 1, ci: list<item: double>
                            child 0, item: double
                        child 2, n: int64
                    child 2, reasoning_first: struct<spec: double, ci: list<item: double>, n: int64>
                        child 0, spec: double
                        child 1, ci: list<item: double>
                            child 0, item: double
                        child 2, n: int64
                child 1, 5-10: struct<freetext: struct<spec: double, ci: list<item: double>, n: int64>, verdict_first: struct<spec: (... 116 chars omitted)
                    child 0, freetext: struct<spec: double, ci: list<item: double>, n: int64>
                        child 0, spec: double
                        child 1, ci: list<item: double>
                            child 0, item: double
                        child 2, n: int64
                    child 1, verdict_first: struct<spec: double, ci: list<item: double>, n: int64>
                        child 0, spec: double
                        child 1, ci: list<item: double>
                            child 0, item
              ...
              pec: double
                child 3, far_ge40_spec: double
              llavamed|verdict_first: struct<slope_logodds_per_mm: double, n: int64, near_le5_spec: double, far_ge40_spec: double>
                child 0, slope_logodds_per_mm: double
                child 1, n: int64
                child 2, near_le5_spec: double
                child 3, far_ge40_spec: double
              medgemma27b|freetext: struct<slope_logodds_per_mm: double, n: int64, near_le5_spec: double, far_ge40_spec: double>
                child 0, slope_logodds_per_mm: double
                child 1, n: int64
                child 2, near_le5_spec: double
                child 3, far_ge40_spec: double
              qwen|verdict_first: struct<slope_logodds_per_mm: double, n: int64, near_le5_spec: double, far_ge40_spec: double>
                child 0, slope_logodds_per_mm: double
                child 1, n: int64
                child 2, near_le5_spec: double
                child 3, far_ge40_spec: double
              medgemma4b|freetext: struct<slope_logodds_per_mm: double, n: int64, near_le5_spec: double, far_ge40_spec: double>
                child 0, slope_logodds_per_mm: double
                child 1, n: int64
                child 2, near_le5_spec: double
                child 3, far_ge40_spec: double
              medgemma27b|verdict_first: struct<slope_logodds_per_mm: double, n: int64, near_le5_spec: double, far_ge40_spec: double>
                child 0, slope_logodds_per_mm: double
                child 1, n: int64
                child 2, near_le5_spec: double
                child 3, far_ge40_spec: double
              medgemma4b|reasoning_first: struct<slope_logodds_per_mm: double, n: int64, near_le5_spec: double, far_ge40_spec: double>
                child 0, slope_logodds_per_mm: double
                child 1, n: int64
                child 2, near_le5_spec: double
                child 3, far_ge40_spec: double
              to
              {'medgemma4b|freetext': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}, 'medgemma4b|verdict_first': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}, 'medgemma4b|reasoning_first': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}, 'medgemma27b|freetext': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}, 'medgemma27b|verdict_first': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}, 'medgemma27b|reasoning_first': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}, 'llavamed|freetext': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}, 'llavamed|verdict_first': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}, 'llavamed|reasoning_first': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}, 'qwen|freetext': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}, 'qwen|verdict_first': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}, 'qwen|reasoning_first': {'slope_logodds_per_mm': Value('float64'), 'n': Value('int64'), 'near_le5_spec': Value('float64'), 'far_ge40_spec': Value('float64')}}
              because column names don't match

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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.

Downloads last month: 35

Total file size:

384 MB