Update README.md

README.md

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+# 🩺 MedMultiPoints: A Multimodal Dataset for Object Detection, Localization, and Counting in Medical Imaging
+
+[![Paper](https://img.shields.io/badge/CBMS-2025-blue)](https://arxiv.org/abs/2505.16647)  
+[![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)  
+📫 For queries, contact: [[email protected]](mailto:[email protected])
+
+---
+
+## 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)
+
+---
+
+## 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
+@article{Gautam2025May,
+	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}},
+	journal = {arXiv},
+	year = {2025},
+	month = may,
+	eprint = {2505.16647},
+	doi = {10.48550/arXiv.2505.16647}
+}
+```