README.md

---
title: VQA Kalbe Bangkit
emoji: 🏆
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.31.5
app_file: app.py
pinned: false
---


# Kalbe Farma - Visual Question Answering (VQA) for Medical Imaging

## Overview
The project addresses the challenge of accurate and efficient medical imaging analysis in healthcare, aiming to reduce human error and workload for radiologists. The proposed solution involves developing advanced AI models for Visual Question Answering (VQA) to assist healthcare professionals in analyzing medical images quickly and accurately. These models will be integrated into a user-friendly web application, providing a practical tool for real-world healthcare settings.

## Dataset
The model is trained using the [Hugging face](https://huggingface.co/datasets/flaviagiammarino/vqa-rad/viewer).



Reference: [ScienceDirect](https://www.sciencedirect.com/science/article/abs/pii/S0933365723001252)

## Model Architecture
The model uses a Parameterized Hypercomplex Shared Encoder network (PHYSEnet).

![Model Architecture](path/to/your/image.png)

Reference: [ScienceDirect](https://www.sciencedirect.com/science/article/abs/pii/S0933365723001252)

## Demo
Please select the example below or upload 4 pairs of mammography exam results.

## Usage

```
cd src

Run the following command on below
Python app.py
```

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference