DeepAI AI Chat
Log In Sign Up

Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization

by   Tian Xia, et al.

Pseudo healthy synthesis, i.e. the creation of a subject-specific `healthy' image from a pathological one, could be helpful in tasks such as anomaly detection, understanding changes induced by pathology and disease or even as data augmentation. We treat this task as a factor decomposition problem: we aim to separate what appears to be healthy and where disease is (as a map). The two factors are then recombined (by a network) to reconstruct the input disease image. We train our models in an adversarial way using either paired or unpaired settings, where we pair disease images and maps (as segmentation masks) when available. We quantitatively evaluate the quality of pseudo healthy images. We show in a series of experiments, performed in ISLES and BraTS datasets, that our method is better than conditional GAN and CycleGAN, highlighting challenges in using adversarial methods in the image translation task of pseudo healthy image generation.


Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images

Understanding the intensity characteristics of brain lesions is key for ...

Eating Healthier: Exploring Nutrition Information for Healthier Recipe Recommendation

With the booming of personalized recipe sharing networks (e.g., Yummly),...

Generator Versus Segmentor: Pseudo-healthy Synthesis

Pseudo-healthy synthesis is defined as synthesizing a subject-specific '...

AFSC: Adaptive Fourier Space Compression for Anomaly Detection

Anomaly Detection (AD) on medical images enables a model to recognize an...

Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation

We propose a novel framework for controllable pathological image synthes...

Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis

Synthesizing a subject-specific pathology-free image from a pathological...