README.md

ImageCLEF2023-MEDVQA-GI-VisionQAries

Repository containing the solution to the Medical Visual Question Answering for GI Task (MEDVQA-GI) in the ImageCLEF 2023 Challenge.

Overview

This repository contains the code and implementation details for the publication titled "Language-based colonoscopy image analysis with pretrained neural networks" which was created for ImageCLEF 2023 Lab: Medical Visual Question Answering for GI Task - MEDVQA-GI.

Publication Information

Title: Language-based colonoscopy image analysis with pretrained neural networks

Authors: Patrycja Cieplicka, Julia Kłos, Maciej Morawski, Jarosław Opała

Getting Started

Data Preparation

Please follow the instructions in data/README.md to download the required data.

Environment Setup (Conda)

Create and activate a new Conda environment:

conda env create -f environment.yml
conda activate image-clef

Running the Code

Task 1 - VQA / Task 2 - VQG

Run the following command to execute the VQA/VQG pipeline:

python3 src/pipeline_vqga.py DATA_PATH MODELS_PATH TRAIN_FLAG INPUT_CONFIG INFERENCE_DATA_PATH INFERENCE_TEXTS_PATH INFERENCE_OUTPUT_PATH

Example:

python3 src/pipeline_vqga.py \
  "data/" \
  "models/" \
  "true" \
  "src/template/vqg_05_dense_8k.yaml" \
  "true" \
  "data/ImageCLEFmed-MEDVQA-GI-2023-Testing-Dataset/images/" \
  "data/inference_answers.txt" \
  "vqg_05_dense_8k.json"

Task 3 - VLQA

• Training: src/train_test.py
• Simple inference: src/predict_test.py
• Evaluation: src/eval_test.py