CXR Segmentation by AdaIN-based Domain Adaptation and Knowledge Distillation

TL;DR

This paper introduces a novel AdaIN-based framework for chest X-ray segmentation that combines domain adaptation and semi-supervised learning through knowledge distillation, effectively handling domain shifts and improving abnormal data segmentation.

Contribution

It proposes a unified AdaIN-based segmentation framework that integrates domain adaptation and semi-supervised learning via task-specific AdaIN codes, enhancing generalization in CXR segmentation.

Findings

Achieves state-of-the-art performance on abnormal CXR segmentation

Demonstrates strong generalizability under domain shift

Effectively utilizes unlabeled data for improved segmentation

Abstract

As segmentation labels are scarce, extensive researches have been conducted to train segmentation networks with domain adaptation, semi-supervised or self-supervised learning techniques to utilize abundant unlabeled dataset. However, these approaches appear different from each other, so it is not clear how these approaches can be combined for better performance. Inspired by recent multi-domain image translation approaches, here we propose a novel segmentation framework using adaptive instance normalization (AdaIN), so that a single generator is trained to perform both domain adaptation and semi-supervised segmentation tasks via knowledge distillation by simply changing task-specific AdaIN codes. Specifically, our framework is designed to deal with difficult situations in chest X-ray radiograph (CXR) segmentation, where labels are only available for normal data, but the trained model should be applied to both normal and abnormal data. The proposed network demonstrates great generalizability under domain shift and achieves the state-of-the-art performance for abnormal CXR segmentation.