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--- |
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splits: |
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- name: train |
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num_bytes: 786835439 |
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num_examples: 10601 |
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download_size: 0 |
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dataset_size: 786835439 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: kvasir-points_datasets_script-train-*.arrow |
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--- |
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# 🩺 MedMultiPoints: A Multimodal Dataset for Object Detection, Localization, and Counting in Medical Imaging |
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[](https://arxiv.org/abs/2505.16647) |
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📫 For queries, contact: [[email protected]](mailto:[email protected]) |
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## Dataset Summary |
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**MedMultiPoints** is a curated, multimodal medical imaging dataset designed for **multi-task learning** in the medical domain—spanning **object detection**, **localization**, and **counting** tasks. It integrates data from **endoscopic** and **microscopic** modalities, reflecting real-world clinical diversity. |
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The dataset is introduced in the paper: |
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**"Point, Detect, Count: Multi-Task Medical Image Understanding with Instruction-Tuned Vision-Language Models"** |
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Presented at **IEEE CBMS 2025, Madrid, Spain.** |
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→ [Project Page & Code](https://github.com/Simula/PointDetectCount) |
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**Instruction-Fused JSONL Files**: |
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- [`multi-task-train.jsonl`](https://huggingface.co/datasets/SimulaMet/MedMultiPoints/resolve/main/instruction_dataset/multi-task-train.jsonl) |
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- [`multi-task-test.jsonl`](https://huggingface.co/datasets/SimulaMet/MedMultiPoints/resolve/main/instruction_dataset/multi-task-test.jsonl) |
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## Features |
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- **10,600 images** from diverse modalities: endoscopy (HyperKvasir) and microscopy (VISEM-Tracking) |
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- Rich **multi-type annotations**: |
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- **Bounding Boxes** (`bbox_2d`) for object detection |
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- **Point Annotations** (`point_2d`) for localization |
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- **Count Labels** (`counts`) for counting tasks |
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- Compatible with **Vision-Language Models (VLMs)** and **instruction-tuned pipelines** |
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- JSON-formatted annotations designed for seamless integration with multimodal training |
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## Data Schema |
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Each sample in the dataset contains: |
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| Field | Type | Description | |
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|-------------------|-----------|--------------------------------------------------| |
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| `image` | Image | Raw medical image | |
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| `image_sha256` | string | SHA-256 hash of the image for integrity | |
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| `img_size` | [int, int]| Original image width and height | |
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| `points` | list | List of `[x, y]` point annotations | |
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| `bbox` | list | List of `[x1, y1, x2, y2]` bounding boxes | |
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| `count` | int | Object count in the image | |
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| `label` | string | Class label (e.g., `polyps`, `sperm`, etc.) | |
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| `collection_method` | string | Task type: `counting`, `detection`, etc. | |
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| `classification` | string | Description of annotation type (e.g., pathological-findings) | |
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| `organ` | string | Target organ: `Lower GI`, `Microscopy`, etc. | |
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## Supported Tasks |
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This dataset supports the following **multi-task** settings: |
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- 🔲 **Object Detection** (bounding box prediction) |
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- 📍 **Localization** (point prediction) |
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- 🔢 **Counting** (object count regression) |
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- 🧠 **Multimodal Instruction-Based Learning** |
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## How to Load |
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```python |
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from datasets import load_dataset |
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ds = load_dataset("SushantGautam/MedMultiPoints")['train'] |
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sample = ds[0] |
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# Access image and annotations |
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image = sample['image'] |
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bbox = sample['bbox'] |
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points = sample['points'] |
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count = sample['count'] |
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``` |
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## Example |
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```json |
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{ |
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"image_sha256": "71179abc4b011cc99bddb3344e3e114765b32bdf77e78892f046026d785a4bdb", |
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"img_size": [622, 529], |
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"points": [[234, 171.5]], |
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"bbox": [[38, 5, 430, 338]], |
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"count": 1, |
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"label": "polyps", |
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"collection_method": "counting", |
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"classification": "pathological-findings", |
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"organ": "Lower GI" |
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} |
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``` |
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## Citation |
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If you use this dataset, please cite: |
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```bibtex |
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@article{Gautam2025May, |
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author = {Gautam, Sushant and Riegler, Michael A. and Halvorsen, P{\aa}l}, |
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title = {{Point, Detect, Count: Multi-Task Medical Image Understanding with Instruction-Tuned Vision-Language Models}}, |
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journal = {arXiv}, |
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year = {2025}, |
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month = may, |
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eprint = {2505.16647}, |
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doi = {10.48550/arXiv.2505.16647} |
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} |
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``` |
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