Privacy Preserving Domain Adaptation for Semantic Segmentation of Medical Images

01/02/2021
by   Serban Stan, et al.
4

Convolutional neural networks (CNNs) have led to significant improvements in tasks involving semantic segmentation of images. CNNs are vulnerable in the area of biomedical image segmentation because of distributional gap between two source and target domains with different data modalities which leads to domain shift. Domain shift makes data annotations in new modalities necessary because models must be retrained from scratch. Unsupervised domain adaptation (UDA) is proposed to adapt a model to new modalities using solely unlabeled target domain data. Common UDA algorithms require access to data points in the source domain which may not be feasible in medical imaging due to privacy concerns. In this work, we develop an algorithm for UDA in a privacy-constrained setting, where the source domain data is inaccessible. Our idea is based on encoding the information from the source samples into a prototypical distribution that is used as an intermediate distribution for aligning the target domain distribution with the source domain distribution. We demonstrate the effectiveness of our algorithm by comparing it to state-of-the-art medical image semantic segmentation approaches on two medical image semantic segmentation datasets.

READ FULL TEXT

page 3

page 8

page 14

research
11/02/2022

Unsupervised Model Adaptation for Source-free Segmentation of Medical Images

The recent prevalence of deep neural networks has lead semantic segmenta...
research
11/18/2021

Edge-preserving Domain Adaptation for semantic segmentation of Medical Images

Domain Adaptation is a technique to address the lack of massive amounts ...
research
12/03/2020

Domain Adaptation of Aerial Semantic Segmentation

Semantic segmentation has achieved significant advances in recent years....
research
05/09/2023

Unsupervised Domain Adaptation for Semantic Segmentation via Feature-space Density Matching

Semantic segmentation is a critical step in automated image interpretati...
research
12/04/2018

Towards Continuous Domain adaptation for Healthcare

Deep learning algorithms have demonstrated tremendous success on challen...
research
12/09/2020

Unsupervised Adversarial Domain Adaptation For Barrett's Segmentation

Barrett's oesophagus (BE) is one of the early indicators of esophageal c...
research
05/11/2020

Target-Independent Domain Adaptation for WBC Classification using Generative Latent Search

Automating the classification of camera-obtained microscopic images of W...

Please sign up or login with your details

Forgot password? Click here to reset