✅ Update (October 2025): The MediaEval Medico 2025 Challenge has concluded.
📊 Competition Results: https://github.com/simula/MediaEval-Medico-2025/blob/main/competition_results.md
🎥 Session recordings: https://www.youtube.com/playlist?list=PLHr-k69ARa0jMZycp19Kefje3dPMG4znR
🙏 Thank you to all participants and contributors!

📋 GitHub Repository | 🔗 MediaEval 2025 | 📝 Registration Form | 🏆 Leaderboard / Registered Submissions

The MediaEval Medico 2025 Challenge 🔬 focuses on Visual Question Answering (VQA) for Gastrointestinal (GI) imaging, emphasizing explainability 🤔📖 to foster trustworthy AI for clinical adoption ⚕️.

This task continues the long-running Medico series at MediaEval, now leveraging the newly developed Kvasir-VQA-x1 dataset, designed to support multimodal reasoning and interpretable clinical decision support 📈.

The MediaEval Workshop 🗣️ was held on: 🗓️ Saturday–Sunday, 25–26 October 2025 | 📍 Dublin, Ireland 🇮🇪 & Online 🌐 (between CMBI 2025 and ACM Multimedia 2025).
📊 Competition Results: https://github.com/simula/MediaEval-Medico-2025/blob/main/competition_results.md 🎥 Recordings: https://www.youtube.com/playlist?list=PLHr-k69ARa0jMZycp19Kefje3dPMG4znR

📈 Goal: Develop AI models that can accurately answer clinical questions using GI endoscopic images.

🧠 The task uses Kvasir-VQA-x1, an advanced dataset comprising 159,549 QA pairs from 6,500 original GI images, featuring:

• Multi-step reasoning questions
• Naturalized medical language
• Complexity scores for curriculum training

🔠 Question Types include:

• Yes/No
• Single-Choice
• Multiple-Choice
• Color-related
• Location-related
• Numerical Count
• Merged reasoning-based questions

💡 Example Training Notebook:
Not sure where to start? Check out: Training with ms-swift

⚠️ Note: You can only submit work for Task 1 if you wish to participate.

It is acceptable to use the full test set for training in your final submission to get competitive score. However, we strongly recommend using proper splits for training and clearly reporting in your paper which splits were used for training, and validation.

📌 Goal: Move beyond simply predicting an answer (Subtask 1) and generate rich, multimodal explanations that are transparent, understandable, and trustworthy for clinicians.

Your system should justify its predictions using multiple complementary reasoning forms—e.g., combining a detailed textual clinical explanation with a visual localization and/or a confidence measure.

Requirements:

• Faithful to the model’s reasoning.
• Clinically relevant and medically sound.
• Useful for real-world decision-making.

from datasets import load_dataset, Image as HfImage

ds = load_dataset("SimulaMet/Kvasir-VQA-x1")["test"]
val_set_task2 = (
    ds.filter(lambda x: x["complexity"] == 1)
      .shuffle(seed=42)
      .select(range(1500))
      .add_column("val_id", list(range(1500)))
      .remove_columns(["complexity", "answer", "original", "question_class"])
      .cast_column("image", HfImage())
)

val_set_task2 is a 🤗 Dataset containing the columns val_id, img_id, image, and question, where image is Pillow Image for easy access.

A JSONL file where each entry corresponds to one test case:

{
  "val_id": "index of validation subset for subtask 2, as in val_set_task2",
  "img_id": "UNIQUE_IMAGE_IDENTIFIER",
  "question": "Original question posed to the model.",
  "answer": "Prediction from your model from Subtask 1.",
  "textual_explanation": "Detailed narrative in clinical language justifying the answer.",
  "visual_explanation": [{
    "type": "heatmap | segmentation_mask | bounding_box | etc.",
    "data": "path/to/visual.png | [[x1,y1,x2,y2]]",
    "description": "(Optional) Highlights the region of interest that supports the answer (e.g., bounding box around the polyp, or heatmap showing focus on mucosal irregularity)."
  }],
  "confidence_score": 0.92
}

Field-by-Field Requirements:

• img_id / question / answer → Must match Subtask 1 data and predictions exactly.
• textual_explanation (Mandatory) → Clinician-oriented reasoning referencing visual cues (location, morphology, color, size, vascular pattern, etc.).
• visual_explanation (Optional but encouraged) → Heatmaps, segmentation masks, or bounding boxes linked to the textual explanation.
• confidence_score (Optional but encouraged) → Float in [0, 1], from model confidence or uncertainty estimation.

1. VLM Self-Probing for Explanations — Ask auxiliary questions (e.g., "What is the abnormality?", "Where is it located?", "Describe its morphology") and combine answers into the textual_explanation.
2. Visual Grounding — Generate heatmaps or attention maps showing influential regions and link them to textual descriptions.
3. Segmentation / Detection — Produce masks or bounding boxes highlighting relevant pathology, reinforcing clinician trust.

⚠️ Participation in Subtask 2 requires completion of Subtask 1.

Built on HyperKvasir and Kvasir-Instrument, the Kvasir-VQA-x1 dataset includes:

• 🧬 159,549 QA pairs
• 🖼️ 6,500 original GI images
• ♻️ 10 weakly augmented images per original (augmentation script provided)
• 🧠 Complexity levels 1–3
• 🧪 Realistic medical question reformulations using LLMs

📥 Dataset: Kvasir-VQA-x1 @ SimulaMet on Hugging Face

Subtask 1 (VQA Performance)

• Metrics: BLEU, ROUGE (1/2/L), METEOR
• Settings: Original & augmented images
• Criteria: Accuracy, relevance, medical correctness

The official challenge score will be computed on a separate hidden challenge set with more metrics. This ensures fairness and that final results truly reflect model performance.

Subtask 2 (Explainability)
Rated by experts on:

1. Answer correctness
2. Clarity & clinical relevance
3. Visual alignment
4. Confidence calibration
5. Methodology & novelty

🚧 Please do not hesitate to contact us if you encounter any issues.

📌 View Registered Submissions

We use the medvqa Python package to validate and submit models to the official system.

pip install -U medvqa

Always use the latest version.

The model that needs to be submitted is expected to be in a HuggingFace repository. Your HuggingFace repo must include a standalone script named:

• submission_task1.py for task 1.
• submission_task2.py for task 2.

Use the provided template script, and make sure to:

• Modify all TODO sections
• Add required information (e.g., model path, inference logic, preprocessing steps) directly in the script
• Keep the required input/output format unchanged

You have two template options for the Task 1 inference script:

• MS-Swift version: submission_task1_swift.py
• PyTorch version: submission_task1.py

Both scripts already include template example code for model loading and inference.

⚠️ Important: Even if you use the MS-Swift template, your final script in the repository must still be named submission_task1.py.

Host your submission in a Hugging Face model repository containing:

• submission_task2.jsonl — one object per val_id
• visuals/ — optional folder with any referenced visual artifacts (heatmaps, masks, boxes as JSON, etc.)
• submission_task2.py file with you team details
• A short README.md explaining how you created the explanations and any post-processing you want to share

Demo submission repo:
https://huggingface.co/SushantGautam/Medico2025_subtask2_demo_submission/tree/main

Naming tips

• Keep data paths in visual_explanation relative to repo root (e.g., visuals/1234_heatmap.png).
• Ensure every val_id in the file corresponds to an item in val_set_task2.

First make sure your submission script works fine in your working environment and it loads the model correctly from your submission repo and generates outputs in the required format.

python submission_task1.py

Next, you can validate the script to work independently. The .py script should now be in the root of the same HuggingFace repo as your model. You can try this in a new venv:

medvqa validate --competition=medico-2025 --task=1/2 --repo_id=<your_repo_id>

• --competition: Set to medico-2025
• --task: Use 1 for Task 1 or 2 for Task 2
• --repo_id: Your HuggingFace model repo ID (e.g., SushantGautam/Florence-2-vqa-demo)

If your code requires extra packages, you must include a requirements.txt in the root of the repo. The system will install these automatically during validation/submission. Else you will get package missing errors.

If validation is okey, you can just run:

medvqa validate_and_submit --competition=medico-2025 --task=1/2 --repo_id=<your_repo_id>

This will make a submisision and your username, along with the task and time, should be visible on the leaderboard for it to be considered officially submitted. The submission library will make your Hugging Face repository public but gated, granting the organizers access to your repo. It must remain unchanged at least until the results of the competition are announced. However, you are free to make your model fully public (non-gated). If you encounter any issues with submission, don’t hesitate to contact us.

• Scripts for augmentation, splits, and baselines
• Submission templates
• Fine-tuned model configs
• Attention & saliency visualization methods

• 📝 April 2025 — Registration for task participation opens ✅
• 📦 May 2025 — Development data release ✅
• 🧪 June 2025 — Test data release ✅
• 📄 24 September 2025 (Wed.) — Runs due
• 📝 8 October 2025 (Wed.) — Working Notes deadline
• 🏫 25–26 October 2025 (Sat.–Sun.) — MediaEval Workshop (Dublin + Online)

• 👨‍🔬 Steven A. Hicks — steven@simula.no
• 🧑‍💻 Michael A. Riegler — michael@simula.no
• 🧑‍🔬 Vajira Thambawita — vajira@simula.no
• 👨‍🏫 Pål Halvorsen — paalh@simula.no
• 🧑‍🎓 Sushant Gautam — sushant@simula.no

Let’s build the future of trustworthy, explainable medical AI.
🌟 GI diagnostics needs interpretable answers. Your model can help save lives.

📍 Register: MediaEval 2025
📁 Repo: GitHub

If you are inspired by the MediaEval Medico 2025 Challenge or the Kvasir-VQA-x1 dataset in your research, please cite the following papers:

@article{Gautam2025Aug,
	author = {Gautam, Sushant and Thambawita, Vajira and Riegler, Michael and others},
	title = {{Medico 2025: Visual Question Answering for Gastrointestinal Imaging}},
	journal = {arXiv},
	year = {2025},
	month = aug,
	eprint = {2508.10869},
	doi = {10.48550/arXiv.2508.10869}
}

@article{Gautam2025Jun,
	author = {Gautam, Sushant and Riegler, Michael A. and Halvorsen, P{\aa}l},
	title = {{Kvasir-VQA-x1: A Multimodal Dataset for Medical Reasoning and Robust MedVQA in Gastrointestinal Endoscopy}},
	journal = {arXiv},
	year = {2025},
	month = jun,
	eprint = {2506.09958},
	doi = {10.48550/arXiv.2506.09958}
}