SYSU1H

Name: SYSU1H
Creator: 深圳技术大学, 中山大学附属第一医院

SYSU1H数据集是一个多模态医学数据集，包含5799个喉镜图像与对应临床报告组成的图像-文本对，数据来源于中山大学附属第一医院，主要用于喉癌早期检测研究，通过融合视觉与文本信息解决喉癌与声带发育不良的形态学相似性问题，提升诊断准确性。该数据集已按8:1:1比例划分为训练、验证和测试集，为医学图像分析领域提供了可靠的实验资源。

深圳技术大学, 中山大学附属第一医院

arXiv

2024-12-24 更新

医学图像分析喉癌检测

基本信息

创建/更新时间

2024-12-24

资源简介

SYSU1H

【数据集背景】

本数据集源站标题为《arXiv Query: search_query=&id_list=2412.18124v1&start=0&max_results=1》。源站首次发布：2024-12-24。

【数据内容】

源站原始描述：The early detection of glottic carcinoma is critical for improving patient outcomes, as it enables timely intervention, preserves vocal function, and significantly reduces the risk of tumor progression and metastasis. However, the similarity in morphology between glottic carcinoma and vocal cord dysplasia results in suboptimal detection accuracy. To address this issue, we propose a vision large language model-based (VisionLLM-based) multimodal fusion network for glottic carcinoma detection, know…

【主题与分类】

主题标签：cs.CV。

【获取与许可】

源站页面：https://arxiv.org/abs/2412.18124v1

原始链接

http://arxiv.org/abs/2412.18124v1

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官方服务

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陕川双基地 × 超十家三甲医院直连

从合规授权、采集治理、专业标注到数据集交付，我们提供全链路闭环服务，让高质量临床数据即拿即用。

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使用方式

数据集说明

SYSU1H 对应论文数据集（arXiv 预印本）。

数据获取指引

打开论文页面获取作者与项目信息：https://arxiv.org/abs/2412.18124v1
论文 Data Availability / Code Availability 章节标注了数据实际托管位置；
获取到实际数据链接后，按对应平台标准方式下载。

论文摘要：Abstract:The early detection of glottic carcinoma is critical for improving patient outcomes, as it enables timely intervention, preserves vocal function, and significantly reduces the risk of tumor progression and metastasis. However, the similarity in morphology between glottic carcinoma and vocal cord dysplasia results in suboptimal detection accuracy. To address this issue, we propose a vision large language model-based (VisionLLM-based) multimodal fusion network for glottic carcinoma detection, known as MMGC-Net. By integrating image and text modalities, multimodal models can capture complementary information, leading to more accurate and robust predictions. In this paper, we collect a private real glottic carcinoma dataset named SYSU1H from the First Affiliated Hospital of Sun Yat-sen University, with 5,799 image-text pairs. We leverage an image encoder and additional Q-Former to extract vision embeddings and the Large Language Model Meta AI (Llama3) to obtain text embeddings. These modalities are then integrated through a laryngeal feature fusion block, enabling a comprehensive integration of image and text features, thereby improving the glottic carcinoma identification performance. Extensive experiments on the SYSU1H dataset demonstrate that MMGC-Net can achieve state-of-the-art performance, which is superior to previous multimodal models.