甲状腺结节超声图像数据集
该数据集包含378个甲状腺结节的超声图像模态数据，由杜克大学从不同超声设备收集并经过临床筛选构建而成，主要用于医学影像分析领域，特别是深度学习算法在甲状腺结节良恶性分类中的性能评估与辅助诊断研究。
基本信息
资源简介
甲状腺结节超声图像数据集
【数据集背景】
本数据集源站标题为《arXiv Query: search_query=&id_list=2207.13765v2&start=0&max_results=1》。源站首次发布：2022-07-27。
【数据内容】
源站原始描述：Objectives: The purpose is to apply a previously validated deep learning algorithm to a new thyroid nodule ultrasound image dataset and compare its performances with radiologists. Methods: Prior study presented an algorithm which is able to detect thyroid nodules and then make malignancy classifications with two ultrasound images. A multi-task deep convolutional neural network was trained from 1278 nodules and originally tested with 99 separate nodules. The results were comparable with that of r…
【数据结构与技术规格】
数据集 DOI：10.1016/j.clinimag.2023.04.010。
【主题与分类】
主题标签：cs.CV、cs.LG、eess.IV。
【获取与许可】
源站页面：https://arxiv.org/abs/2207.13765v2
下载信息
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使用方式
数据集说明
甲状腺结节超声图像数据集 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/2207.13765v2
- 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:Objectives: The purpose is to apply a previously validated deep learning algorithm to a new thyroid nodule ultrasound image dataset and compare its performances with radiologists. Methods: Prior study presented an algorithm which is able to detect thyroid nodules and then make malignancy classifications with two ultrasound images. A multi-task deep convolutional neural network was trained from 1278 nodules and originally tested with 99 separate nodules. The results were comparable with that of radiologists. The algorithm was further tested with 378 nodules imaged with ultrasound machines from different manufacturers and product types than the training cases. Four experienced radiologists were requested to evaluate the nodules for comparison with deep learning. Results: The Area Under Curve (AUC) of the deep learning algorithm and four radiologists were calculated with parametric, binormal estimation. For the deep learning algorithm, the AUC was 0.69 (95% CI: 0.64 - 0.75). The AUC of radiologists were 0.63 (95% CI: 0.59 - 0.67), 0.66 (95% CI:0.61 - 0.71), 0.65 (95% CI: 0.60 - 0.70), and 0.63 (95%CI: 0.58 - 0.67). Conclusion: In the new testing dataset, the deep learning algorithm achieved similar performances with all four radiologists. The relative performance difference between the algorithm and the radiologists is not significantly affected by the difference of ultrasound scanner.
数据缺失？
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