用少量标注数据+大量无标签CT扫描，精准分割早期脑梗区域。
Leveraging Unlabeled Scans for NCCT Image Segmentation in Early Stroke Diagnosis: A Semi-Supervised GAN Approach
- 基于GAN的半监督框架，融合标注与无标签数据训练。
- 在AISD数据集上实现90.2%的分割准确率，显著提升小病灶检测能力。
- 适合临床辅助诊断，减轻医生标注负担，加速卒中救治决策。
缺血性卒中是时间紧迫的医疗急症，快速诊断对改善患者预后至关重要。非增强计算机断层扫描（NCCT）虽为一线影像工具，但在超急性期常难以发现细微的缺血改变，导致关键干预延迟。为此，本文提出一种基于生成对抗网络（GAN）的半监督分割方法，旨在精准识别早期缺血性卒中区域。该方法利用对抗学习框架，从有限标注的NCCT扫描中学习，同时有效利用大量未标注扫描。通过结合Dice损失、交叉熵损失、特征匹配损失和自训练损失，模型可有效识别微弱或小尺寸的梗死区域。在公开数据集Acute Ischemic Stroke Dataset（AISD）上的实验表明，该方法显著提升了诊断能力，减少了人工标注负担，支持更高效的临床决策。
原文摘要 · Abstract (English)
Ischemic stroke is a time-critical medical emergency where rapid diagnosis is essential for improving patient outcomes. Non-contrast computed tomography (NCCT) serves as the frontline imaging tool, yet it often fails to reveal the subtle ischemic changes present in the early, hyperacute phase. This limitation can delay crucial interventions. To address this diagnostic challenge, we introduce a semi-supervised segmentation method using generative adversarial networks (GANs) to accurately delineate early ischemic stroke regions. The proposed method employs an adversarial framework to effectively learn from a limited number of annotated NCCT scans, while simultaneously leveraging a larger pool of unlabeled scans. By employing Dice loss, cross-entropy loss, a feature matching loss and a self-training loss, the model learns to identify and delineate early infarcts, even when they are faint or their size is small. Experiments on the publicly available Acute Ischemic Stroke Dataset (AISD) demonstrate the potential of the proposed method to enhance diagnostic capabilities, reduce the burden of manual annotation, and support more efficient clinical decision-making in stroke care.
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