Datasets:

erow
/

Medical-ROIs-K2.6

pretty_name: Medical-ROIs-K2.6
license: other
task_categories:
  - visual-question-answering
  - image-to-text
tags:
  - medical
  - radiology
  - pathology
  - roi
  - grounding
  - bounding-box
  - multimodal
  - sft
size_categories:
  - 10K<n<100K
language:
  - en
dataset_info:
  features:
    - name: id
      dtype: string
    - name: source
      dtype: string
    - name: teacher_model
      dtype: string
    - name: teacher_provenance
      dtype: string
    - name: images
      sequence: image
    - name: messages
      list:
        - name: role
          dtype: string
        - name: content
          dtype: string
    - name: messages_mm
      dtype: string
    - name: problem
      dtype: string
    - name: solution
      dtype: string
    - name: prompt
      dtype: string
    - name: response
      dtype: string
    - name: seeing_regions
      list:
        - name: title
          dtype: string
        - name: bbox
          sequence: int64
    - name: seeing_json
      dtype: string
    - name: reasoning
      dtype: string
    - name: n_images
      dtype: int64
    - name: index
      dtype: int64
  splits:
    - name: train
      num_examples: 25134
    - name: validation
      num_examples: 2000
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*

Medical-ROIs-K2.6

Medical visual grounding SFT data: for each clinical VQA sample, a teacher model proposes answer-supporting ROIs (regions of interest) as 2D bounding boxes.

Teacher: moonshotai/Kimi-K2.6.
Upstream images & QA: MBZUAI/medix-rl-data.

How the data is generated

MBZUAI/medix-rl-data (train)
        │
        │  problem / solution / image / source / id
        ▼
  Teacher: moonshotai/Kimi-K2.6
  (vision + text; given question + gold answer)
        │
        │  ROI XML: <region><title>…</title><bbox>[x1,y1,x2,y2]</bbox></region>
        ▼
  JSONL (sft.jsonl)  →  stratified split
        │                 ├── sft_train.jsonl  (25,134)
        │                 └── sft_val.jsonl    (2,000)
        ▼
  prepare_hf: join JSONL text with medix-rl-data images
        │
        ▼
  Hugging Face DatasetDict  (this repo)

1. Source VQA pool

Rows come from MBZUAI/medix-rl-data (train, 51,335 examples) with four upstream sources:

source	role
`RadGenome/PMC-VQA`	PubMed Central figure VQA
`flaviagiammarino/path-vqa`	Pathology VQA
`BoKelvin/SLAKE`	Radiology VQA (SLAKE)
`flaviagiammarino/vqa-rad`	VQA-RAD

Each row provides id, image, problem (question), solution (gold answer), and source.

2. Teacher ROI annotation

A multimodal teacher (moonshotai/Kimi-K2.6) is prompted with the image, clinical question, and gold answer, and asked to box the visual evidence that supports the answer.

Output format (one or more blocks):

<region><title>caption</title><bbox>[x1,y1,x2,y2]</bbox></region>

BBox convention: integers in [0, 1000], top-left origin; require x1 < x2, y1 < y2.
Successful annotations are written to JSONL (sft.jsonl); failures are logged for repair/retry.
This release keeps the successfully annotated subset (27,134 rows, ≈53% of the upstream train pool).

3. Train / validation split

From sft.jsonl, a source-stratified random split (seed=42) produces:

split	file	count
train	`sft_train.jsonl`	25,134
validation	`sft_val.jsonl`	2,000

4. Hugging Face packaging

medix_seeing_sft.prepare_hf joins each JSONL row with the matching image(s) from MBZUAI/medix-rl-data by id, and materializes a DatasetDict with multimodal chat fields suitable for SFT loaders.

Splits in this repo

split	#examples
`train`	25,134
`validation`	2,000

Source mix (approx.)

source	train	validation
RadGenome/PMC-VQA	12,315	980
flaviagiammarino/path-vqa	9,535	759
BoKelvin/SLAKE	2,391	190
flaviagiammarino/vqa-rad	893	71

Schema

field	description
`id`	Sample id (matches `medix-rl-data`)
`source`	Upstream VQA dataset name
`images`	Image list (`datasets.Image`)
`messages`	Chat turns with string content (`<image>` placeholders)
`messages_mm`	JSON string of multimodal content-list messages
`problem` / `solution`	Clinical question and gold answer
`prompt` / `response`	User prompt and assistant ROI XML
`seeing_regions`	Structured `{title, bbox}` list
`seeing_json`	JSON serialization of regions
`reasoning`	Teacher reasoning text (when available)
`teacher_model`	`moonshotai/Kimi-K2.6`
`teacher_provenance`	`distilled_from:moonshotai/Kimi-K2.6`
`n_images`	Number of images
`index`	Annotation-run index

Example assistant target

<region><title>Right kidney</title><bbox>[130,380,470,720]</bbox></region>
<region><title>Left kidney</title><bbox>[570,420,840,750]</bbox></region>

Load

from datasets import load_dataset

ds = load_dataset("erow/Medical-ROIs-K2.6")
print(ds["train"][0]["problem"])
print(ds["train"][0]["response"])
ds["train"][0]["images"][0]  # PIL.Image

Or from a local save_to_disk folder:

from datasets import load_from_disk
ds = load_from_disk("/path/to/hf_seeing_sft")

Intended use

Supervised fine-tuning / distillation of medical VLMs for visual grounding: predicting ROIs that evidence the answer to a clinical question (complementary to answer-only VQA).

Limitations

ROIs are teacher-generated, not human-verified for every sample; some boxes may be loose, panel-misaligned, or weakly linked to the QA.
Coverage is a subset of medix-rl-data (annotation failures / rate limits excluded).
Not a clinical decision-support product; for research use only.

Citation / provenance

Upstream pool: MBZUAI/medix-rl-data and its constituent VQA datasets (PMC-VQA, PathVQA, SLAKE, VQA-RAD).
Teacher: Moonshot AI Kimi K2.6 (via OpenAI-compatible inference).
Pipeline: MediX seeing-SFT (medix_seeing_sft generate → split → prepare_hf).