TTGA

KO | EN

gitlite — search

#javascript #hacktoberfest #go #machine-learning #rust #linux #macos #java #windows #awesome #cli #kubernetes #vue #pytorch

(★ 16)

[MedIA 2026] Official implementation of TTGA: Test-Time Generative Augmentation for Medical Image Segmentation.

TTGA 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Test-Time Generative Augmentation (TTGA)

> **[MedIA 2026]** Official PyTorch Implementation of "**Test-Time Generative Augmentation for Medical Image Segmentation**" (Medical Image Analysis).

[![MedIA](https://img.shields.io/badge/MedIA-Accepted-firebrick.svg)](https://doi.org/10.1016/j.media.2025.103902)
[![DOI](https://img.shields.io/badge/DOI-10.1016%2Fj.media.2025.103902-blue.svg)](https://doi.org/10.1016/j.media.2025.103902)
[![arXiv](https://img.shields.io/badge/arXiv-2406.17608-b31b1b.svg)](https://arxiv.org/abs/2406.17608)
[![License: CC BY-NC-ND 4.0](https://img.shields.io/badge/License-CC%20BY--NC--ND%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc-nd/4.0/)

## 📢 News
* **[2026.01.06]** 🎆 **Happy New Year!** The [arXiv preprint](https://arxiv.org/abs/2406.17608) has been updated.
* **[2025.12.25]** 🎄 **Merry Christmas!** We have updated the Citation and [official link](https://doi.org/10.1016/j.media.2025.103902). The paper is now fully indexed as **Medical Image Analysis (MedIA), Vol. 109, 2026**.
* **[2025.12.02]** 🎉 **Accepted!** Our paper has been formally accepted by **Medical Image Analysis (MedIA)**!
* **[2025.07.15]** 🛠️ **Code Release**: We have released the complete training codes and inference demos.

## Introduction
Test-Time Generative Augmentation (TTGA) is a novel approach to enhance medical image segmentation during test time. Instead of employing handcrafted transforms or functions on the input test image to create multiple views for test-time augmentation, this approach advocate for the utilization of an advanced domain-fine-tuned generative model, e.g., diffusion models, for test-time augmentation. Hence, by integrating the generative model into test-time augmentation, we can effectively generate multiple views of a given test sample, aligning with the content and appearance characteristics of the sample and the related local data distribution.

## Pipeline
The proposed pipeline of three key workflows are presented. The test image is processed through a sequence of steps to generate a noise image at a designated step count. Using this noise image, a one-step denoising process is employed to refine a trainable null-text embedding, enabling the stable generation of results that closely resemble the initial image. In the augmentation generation phase, this null-text embedding, guided by semantic and regional information, is leveraged to produce a series of augmented images.
<img src="figs/fig-2.png">

## Installation
This repository requires CUDA 12.1, Python 3.9 and Pytorch 2.1. To install the latest version run:
1. Create Conda Environment and Install Dependencies
  ```
  conda create --name ttga python=3.9
  conda activate ttga
  pip install -r requirements.txt
  ```
2. Install diffusers
- Download diffusers==0.27.0 and extract its contents. You can do this using command-line tools:
```
wget https://pypi.org/packages/source/d/diffusers/diffusers-0.27.0.tar.gz
tar -xzf diffusers-0.27.0.tar.gz
mv diffusers-0.27.0 diffusers
cd diffusers
python setup.py install
cd ..
```

## Running
This section outlines the steps to run the processes described in this repository, using the **polyp** dataset as an example.

**Step 1: LoRA Fine-tuning of Stable Diffusion**
First, fine-tune Stable Diffusion using `LoRA` on the polyp dataset:
```
bash scripts/lora_polyp.sh
```

**Step 2: One-step Null-Text Optimization**
Next, perform one-step null-text optimization on test images to extract their identity information.

**Step 3: Generative Augmentation**
Finally, apply generative augmentation to the test images.

**Batch Operations and Demos:**
- For **batch operations** of **Step 2** and **Step 3**, please refer to [`main.py`](main.py). This script provides a comprehensive implementation for processing multiple images.
- A **demonstration** showcasing these processes can be found in [`demo.ipynb`](demo.ipynb). This notebook provides an interactive example of how to apply these techniques.

## Augmentation
:sparkles: Optic Disc and Cup Segmentation

:sparkles: Polyp Segmentation

:sparkles: Skin Lesion Segmentation

## Materials
:two_hearts: SOTA segmentation models with codes, datasets and open-source parameters. (Thanks!)

## Citation
If you find our work useful in your research, please consider citing:

```bibtex
@article{ma2026ttga,
  title = {Test-time generative augmentation for medical image segmentation},
  journal = {Medical Image Analysis},
  volume = {109},
  pages = {103902},
  year = {2026},
  issn = {1361-8415},
  doi = {10.1016/j.media.2025.103902},
  author = {Ma, Xiao and Tao, Yuhui and Zhang, Zetian and Zhang, Yuhan and Wang, Xi and Zhang, Sheng and Ji, Zexuan and Zhang, Yizhe and Chen, Qiang and Yang, Guang}
}

TTGA

TTGA 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

TTGA

TTGA 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README