| --- |
| 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`](https://huggingface.co/datasets/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): |
|
|
| ```text |
| <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 |
|
|
| ```text |
| <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 |
|
|
| ```python |
| 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: |
|
|
| ```python |
| 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](https://huggingface.co/datasets/MBZUAI/medix-rl-data) and its constituent VQA datasets (PMC-VQA, PathVQA, SLAKE, VQA-RAD). |
| - Teacher: [Moonshot AI Kimi K2.6](https://huggingface.co/moonshotai/Kimi-K2.6) (via OpenAI-compatible inference). |
| - Pipeline: MediX seeing-SFT (`medix_seeing_sft` generate → split → `prepare_hf`). |
|
|
