Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation

TL;DR

Duo-SegNet introduces an adversarial dual-view semi-supervised learning framework for medical image segmentation, effectively reducing the need for extensive pixel-level annotations and outperforming existing methods.

Contribution

It presents a novel adversarial dual-view training approach with a critic, enhancing semi-supervised segmentation performance in medical imaging.

Findings

Outperforms state-of-the-art segmentation algorithms

Works effectively with limited labeled data

Demonstrates robustness across multiple datasets

Abstract

Segmentation of images is a long-standing challenge in medical AI. This is mainly due to the fact that training a neural network to perform image segmentation requires a significant number of pixel-level annotated data, which is often unavailable. To address this issue, we propose a semi-supervised image segmentation technique based on the concept of multi-view learning. In contrast to the previous art, we introduce an adversarial form of dual-view training and employ a critic to formulate the learning problem in multi-view training as a min-max problem. Thorough quantitative and qualitative evaluations on several datasets indicate that our proposed method outperforms state-of-the-art medical image segmentation algorithms consistently and comfortably. The code is publicly available at https://github.com/himashi92/Duo-SegNet