| --- |
| splits: |
| - name: train |
| num_bytes: 786835439 |
| num_examples: 10601 |
| download_size: 0 |
| dataset_size: 786835439 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: kvasir-points_datasets_script-train-*.arrow |
| --- |
| |
| # 🩺 MedMultiPoints: A Multimodal Dataset for Object Detection, Localization, and Counting in Medical Imaging |
|
|
| [](https://arxiv.org/abs/2505.16647) |
| 📫 For queries, contact: [sushant@simula.no](mailto:sushant@simula.no) |
|
|
| ## Dataset Summary |
|
|
| **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. |
|
|
| The dataset is introduced in the paper: |
| **"Point, Detect, Count: Multi-Task Medical Image Understanding with Instruction-Tuned Vision-Language Models"** |
| Presented at **IEEE CBMS 2025, Madrid, Spain.** |
| → [Project Page & Code](https://github.com/Simula/PointDetectCount) |
|
|
| **Instruction-Fused JSONL Files**: |
|
|
| - [`multi-task-train.jsonl`](https://huggingface.co/datasets/SimulaMet/MedMultiPoints/resolve/main/instruction_dataset/multi-task-train.jsonl) |
| - [`multi-task-test.jsonl`](https://huggingface.co/datasets/SimulaMet/MedMultiPoints/resolve/main/instruction_dataset/multi-task-test.jsonl) |
|
|
|
|
| ## Features |
|
|
| - **10,600 images** from diverse modalities: endoscopy (HyperKvasir) and microscopy (VISEM-Tracking) |
| - Rich **multi-type annotations**: |
| - **Bounding Boxes** (`bbox_2d`) for object detection |
| - **Point Annotations** (`point_2d`) for localization |
| - **Count Labels** (`counts`) for counting tasks |
| - Compatible with **Vision-Language Models (VLMs)** and **instruction-tuned pipelines** |
| - JSON-formatted annotations designed for seamless integration with multimodal training |
|
|
| ## Data Schema |
|
|
| Each sample in the dataset contains: |
|
|
| | Field | Type | Description | |
| |-------------------|-----------|--------------------------------------------------| |
| | `image` | Image | Raw medical image | |
| | `image_sha256` | string | SHA-256 hash of the image for integrity | |
| | `img_size` | [int, int]| Original image width and height | |
| | `points` | list | List of `[x, y]` point annotations | |
| | `bbox` | list | List of `[x1, y1, x2, y2]` bounding boxes | |
| | `count` | int | Object count in the image | |
| | `label` | string | Class label (e.g., `polyps`, `sperm`, etc.) | |
| | `collection_method` | string | Task type: `counting`, `detection`, etc. | |
| | `classification` | string | Description of annotation type (e.g., pathological-findings) | |
| | `organ` | string | Target organ: `Lower GI`, `Microscopy`, etc. | |
|
|
|
|
| ## Supported Tasks |
|
|
| This dataset supports the following **multi-task** settings: |
|
|
| - 🔲 **Object Detection** (bounding box prediction) |
| - 📍 **Localization** (point prediction) |
| - 🔢 **Counting** (object count regression) |
| - 🧠 **Multimodal Instruction-Based Learning** |
|
|
| ## How to Load |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("SushantGautam/MedMultiPoints")['train'] |
| sample = ds[0] |
| |
| # Access image and annotations |
| image = sample['image'] |
| bbox = sample['bbox'] |
| points = sample['points'] |
| count = sample['count'] |
| ``` |
|
|
|
|
| ## Example |
|
|
| ```json |
| { |
| "image_sha256": "71179abc4b011cc99bddb3344e3e114765b32bdf77e78892f046026d785a4bdb", |
| "img_size": [622, 529], |
| "points": [[234, 171.5]], |
| "bbox": [[38, 5, 430, 338]], |
| "count": 1, |
| "label": "polyps", |
| "collection_method": "counting", |
| "classification": "pathological-findings", |
| "organ": "Lower GI" |
| } |
| ``` |
|
|
|
|
| ## Citation |
|
|
| If you use this dataset, please cite: |
|
|
| ```bibtex |
| @incollection{Gautam, |
| author = {Gautam, Sushant and Riegler, Michael A. and Halvorsen, P{\aa}l}, |
| title = {{Point, Detect, Count: Multi-Task Medical Image Understanding with Instruction-Tuned Vision-Language Models}}, |
| booktitle = {{2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS)}}, |
| journal = {Published in: 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS)}, |
| pages = {18--20}, |
| publisher = {IEEE}, |
| doi = {10.1109/CBMS65348.2025.00090} |
| } |
| ``` |
|
|
