DeepAI
Log In Sign Up

An Asymetric Cycle-Consistency Loss for Dealing with Many-to-One Mappings in Image Translation: A Study on Thigh MR Scans

04/23/2020
by   Michael Gadermayr, et al.
0

Generative adversarial networks using a cycle-consistency loss facilitate unpaired training of image-translation models and thereby exhibit a very high potential in manifold medical applications. However, the fact that images in one domain potentially map to more than one image in another domain (e.g. in case of pathological changes) exhibits a major challenge for training the networks. In this work, we offer a solution to improve the training process in case of many-to-one mappings by modifying the cycle-consistency loss. We show formally and empirically that the proposed method improves the performance significantly without radically changing the architecture and without increasing the overall complexity. We evaluate our method on thigh MRI scans with the final goal of segmenting the muscle in fat-infiltrated patients' data.

READ FULL TEXT

page 4

page 7

02/13/2018

An Optimized Architecture for Unpaired Image-to-Image Translation

Unpaired Image-to-Image translation aims to convert the image from one d...
07/06/2020

MCMI: Multi-Cycle Image Translation with Mutual Information Constraints

We present a mutual information-based framework for unsupervised image-t...
08/26/2018

Twin-GAN -- Unpaired Cross-Domain Image Translation with Weight-Sharing GANs

We present a framework for translating unlabeled images from one domain ...
10/13/2019

Generative Image Translation for Data Augmentation in Colorectal Histopathology Images

We present an image translation approach to generate augmented data for ...
08/12/2022

CycleGAN with three different unpaired datasets

The original publication Unpaired Image-to-Image Translation using Cycle...
07/06/2020

A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRI

We propose a novel convolutional method for the detection and identifica...
06/28/2022

Stain Isolation-based Guidance for Improved Stain Translation

Unsupervised and unpaired domain translation using generative adversaria...