Test-Time Generative Augmentation (TTGA)

This is the official repository for "Test-Time Generative Augmentation for Medical Image Segmentation"

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.

Augmentation

✨ Optic Disc and Cup Segmentation

✨ Polyp Segmentation

✨ Skin Lesion Segmentation

Materials

💕 SOTA segmentation models with codes, datasets and open-source parameters. (Thanks!)

Index	Physiology	Dataset	Paper	Code
1	Optic Disc and Cup	REFUGE20	Segtrain	code
2	Polyp	Kvasir CVC-ClinicDB CVC-ColonDB CVC-300 ETIS-LaribPolypDB	HSNet	code
3	Skin Lesion	ISIC 2017 ISIC 2018	TMUnet	code

Citing

TO-DO.