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README.md

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-# Skin Lesion Segmentation API with Uncertainty Estimation
-
-This repository contains a production-ready FastAPI service for performing semantic segmentation of skin lesions, trained on the ISIC 2018 dataset. The segmentation model uses a Swin-Tiny transformer encoder with a custom UPerNet decoder, and incorporates Monte Carlo (MC) Dropout at inference time to estimate predictive uncertainty.
-
-## Features
-
-- RESTful FastAPI endpoint for real-time image segmentation
-- Swin-Tiny + UPerNet architecture for accurate lesion boundary detection
-- Monte Carlo Dropout-based uncertainty quantification
-- Heatmap visualization of model uncertainty using OpenCV colormaps
-- Side-by-side output of the predicted segmentation mask and uncertainty map
-- Clean, modular codebase suitable for further extension and deployment
-
-## Inference Workflow
-
-1. The API accepts a dermoscopic image uploaded via a POST request to the `/predict/` endpoint.
-2. The image is resized, normalized, and passed through the segmentation model.
-3. The model performs multiple stochastic forward passes using MC Dropout.
-4. The mean prediction is binarized to produce a segmentation mask.
-5. The standard deviation across predictions is visualized as a heatmap to express uncertainty.
-6. The API returns both the binary mask and uncertainty heatmap as a combined PNG image.
-
-## File Structure
-
-| File              | Description                                                  |
-|-------------------|--------------------------------------------------------------|
-| `main.py`         | FastAPI application and endpoint logic                       |
-| `model.py`        | Model architecture and MC Dropout inference utilities        |
-| `requirements.txt`| Required Python packages                                     |
-| `.gitignore`      | Specifies ignored files (e.g., `venv/`, model weights)       |
-
-## Requirements
-
-- Python 3.9+
-- PyTorch
-- FastAPI
-- OpenCV
-- Pillow
-- timm
-- torchvision
-- uvicorn
-
-Install dependencies:
-
-```bash
-pip install -r requirements.txt
-```
-
-
-## Model Weights
-
-To run the API, you will need to download the pretrained model weights and place the file in the root directory of this repository.
-
-**Download link:**
-
-[best_swin_upernet_main.pth](https://huggingface.co/samyakshrestha/swin-medical-segmentation/resolve/main/best_swin_upernet_main.pth?download=true)
-
-After downloading, ensure the file is named exactly "best_swin_upernet_main.pth" and saved in the same directory as `main.py` and `model.py`.
-
-This weight file contains the Swin-Tiny UPerNet model trained on the ISIC 2018 skin lesion segmentation dataset.
-
-
-## Dataset
-
-This project uses the ISIC 2018 Challenge Dataset for training, which contains dermoscopic images of skin lesions annotated for binary segmentation.
-
-Dataset access: https://challenge.isic-archive.com/data/
-
-## Docker Support
-
-This project includes a `Dockerfile` for containerized deployment. Run the API with Docker:
-
-### Build the Docker image
-```bash
-docker build -t medical-segmentation-api .
-```
-
-### Run the container
-```bash
-docker run -p 8000:8000 medical-segmentation-api
-```
-
-
-The API will then be available at:
-http://localhost:8000/predict/
-
-## Author
-
-Samyak Shrestha